From 42b4f59d4a141304b3cbed876254dbc4dbfc1072 Mon Sep 17 00:00:00 2001
From: Parth Danve <parth.danve@uconn.edu>
Date: Tue, 11 Aug 2026 16:19:23 -0400
Subject: [PATCH 01/25] AQC+Trotter Tutorial + Template

---
 .../function-template-aqc-trotter.ipynb       | 575 ++++++++++++
 ...on-scattering-with-a-qiskit-function.ipynb | 885 ++++++++++++++++++
 2 files changed, 1460 insertions(+)
 create mode 100644 docs/guides/function-template-aqc-trotter.ipynb
 create mode 100644 docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function.ipynb

diff --git a/docs/guides/function-template-aqc-trotter.ipynb b/docs/guides/function-template-aqc-trotter.ipynb
new file mode 100644
index 00000000000..8e9e3c77d83
--- /dev/null
+++ b/docs/guides/function-template-aqc-trotter.ipynb
@@ -0,0 +1,575 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "frontmatter",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "title: Deploy and run the AQC-Trotter-dynamics Qiskit Function\n",
+    "description: Deploy the AQC + Trotter Hamiltonian dynamics function template to IBM Quantum Serverless, then run it on a simulator and on a QPU.\n",
+    "---\n",
+    "\n",
+    "{/* cspell:ignore Trotter Trotterization quimb cotengra cotengrust Suzuki fidelities isa */}\n",
+    "\n",
+    "# Deploy and run the AQC-Trotter-dynamics Qiskit Function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "overview",
+   "metadata": {},
+   "source": [
+    "## Overview\n",
+    "\n",
+    "This is an experiment-agnostic Qiskit Function for Hamiltonian dynamics. Given a 1D nearest-neighbor Pauli Hamiltonian, a prepared initial state (optional), and a set of observables, it runs Trotter time-evolution, approximate quantum compilation (AQC) circuit compression, and mitigated execution, then returns each observable's time series. Swap the setup (PRE) and the analysis (POST) and the same core drives a different experiment:\n",
+    "\n",
+    "```\n",
+    "   PRE (your setup)            FUNCTION (deployed here)             POST (your analysis)\n",
+    " prepare a state         ->  Trotter -> AQC compress -> execute  ->  S(q, w) (neutron)\n",
+    " (circuit / product)        (statevector / fake / runtime)          magnetization, transport,\n",
+    " + optional local kick      -> <O>(t)                               quench dynamics, ...\n",
+    "```\n",
+    "\n",
+    "The template is published in the Qiskit function templates repository, alongside the other application templates. See: [AQC Dynamics Template](https://github.com/qiskit-community/qiskit-function-templates/tree/main/physics/aqc_trotter). This notebook deploys it to your own IBM Quantum&reg; Serverless account. Run it once, and any notebook can then call the function with `serverless.load(\"aqc-dynamics-function\")`.\n",
+    "\n",
+    "For a worked scientific example, see [Simulate neutron scattering with an AQC-dynamics Qiskit Function](neutron-scattering-with-qiskit-functions.ipynb), which calls this function to compute the dynamical structure factor of KCuF$_3$. This notebook covers deployment and the input contract instead."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "requirements",
+   "metadata": {},
+   "source": [
+    "## Requirements\n",
+    "\n",
+    "Before starting, be sure you have the following in this notebook's kernel environment:\n",
+    "\n",
+    "- Qiskit SDK v2.0 or later (`pip install qiskit`).\n",
+    "- The Qiskit IBM Catalog client (`pip install qiskit-ibm-catalog`), which uploads and runs Qiskit Functions.\n",
+    "\n",
+    "The function's own scientific dependencies (`qiskit-addon-aqc-tensor`, `cotengrust`, `qiskit-aer`) do not need to be installed locally."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "source-files",
+   "metadata": {},
+   "source": [
+    "## Get the template source files\n",
+    "\n",
+    "The function is a small Python package that Qiskit Serverless runs in the cloud, so its source has to exist as local files that are uploaded at deploy time. The package is published in the Qiskit function templates repository.\n",
+    "\n",
+    "Download **[`source_files`](https://download-directory.github.io/?url=https%3A%2F%2Fgithub.com%2Fqiskit-community%2Fqiskit-function-templates%2Ftree%2Fmain%2Fphysics%2Faqc_trotter%2Fsource_files)**\n",
+    "\n",
+    "The download is a single zip, named after the full path of the directory in the repository:\n",
+    "\n",
+    "`qiskit-community qiskit-function-templates main physics aqc_trotter source_files.zip`\n",
+    "\n",
+    "1. Unzip it into the directory that holds this notebook.\n",
+    "2. Rename the extracted folder from that long name to `source_files`.\n",
+    "\n",
+    "Your working directory then looks like this:\n",
+    "\n",
+    "```\n",
+    "your-working-directory/\n",
+    "├── deploy-aqc-dynamics-function.ipynb    <- this notebook\n",
+    "└── source_files/                         <- the renamed folder\n",
+    "    ├── __init__.py\n",
+    "    ├── program.py\n",
+    "    └── source/\n",
+    "        ├── __init__.py\n",
+    "        ├── _serverless.py\n",
+    "        ├── app_function.py\n",
+    "        ├── aqc.py\n",
+    "        ├── build.py\n",
+    "        ├── execute.py\n",
+    "        └── hamiltonian.py\n",
+    "```\n",
+    "\n",
+    "The name has to be exactly `source_files`, because that is the `working_dir` Step 3 uploads.\n",
+    "\n",
+    "`program.py` is the entry point the gateway invokes. Everything under `source/` is the implementation, split by stage: Hamiltonian and Trotter synthesis, AQC compression, and execution. None of it needs editing to run the examples below. Step 3 uploads the whole directory, so repeat that step whenever you change a file."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "auth-md",
+   "metadata": {},
+   "source": [
+    "## 1. Authentication\n",
+    "\n",
+    "Use `qiskit-ibm-catalog` to authenticate to `QiskitServerless` with your API key (token) and CRN (instance), which you can find on the [IBM Quantum Platform](https://quantum.cloud.ibm.com) dashboard. This will allow you to locally instantiate the serverless client to upload or run the selected function:\n",
+    "\n",
+    "```python\n",
+    "from qiskit_ibm_catalog import QiskitServerless\n",
+    "serverless = QiskitServerless(channel=\"ibm_quantum_platform\", token=\"MY_TOKEN\", instance=\"MY_CRN\")\n",
+    "```\n",
+    "\n",
+    "You can optionally use `save_account()` to save your credentials in your local environment (see the [Set up your IBM Cloud account](/docs/guides/cloud-setup#cloud-save) guide). Note that this writes your credentials to the same file as [`QiskitRuntimeService.save_account()`](/docs/api/qiskit-ibm-runtime/qiskit-runtime-service#save_account):\n",
+    "\n",
+    "```python\n",
+    "QiskitServerless.save_account(channel=\"ibm_quantum_platform\", token=\"MY_TOKEN\", instance=\"MY_CRN\")\n",
+    "```\n",
+    "\n",
+    "If the account is saved, there is no need to provide the token to authenticate:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "auth-code",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit_ibm_catalog import QiskitServerless\n",
+    "\n",
+    "# Authenticate to the remote cluster\n",
+    "# In this case, loading a saved account\n",
+    "serverless = QiskitServerless()\n",
+    "\n",
+    "# REPLACE WITH YOUR OWN CREDENTIALS or SAVED ACCOUNT\n",
+    "# serverless = QiskitServerless(channel=\"ibm_quantum_platform\", token=\"MY_TOKEN\", instance=\"MY_CRN\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "deps-md",
+   "metadata": {},
+   "source": [
+    "## 2. Declare dependencies\n",
+    "\n",
+    "Packages the function needs on top of the managed base serverless image.\n",
+    "\n",
+    "> The gateway only installs names on its allowlist ([`requirements-dynamic-dependencies.txt`](https://github.com/Qiskit/qiskit-serverless/blob/main/docker-images/requirements-dynamic-dependencies.txt)), matched by package name and pinned to the allowed version with `==`. Anything else must arrive transitively (as a dependency of an allowlisted package). `[extras]` *are* honored — `qiskit-addon-aqc-tensor[quimb-jax]` is what drags `quimb` / `jax` in here. `cotengrust` is needed for memory efficiency during tensor network simulation. `qiskit-aer` is listed separately for the `fake` backend (local noisy simulation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "deps-code",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DEPENDENCIES = [\n",
+    "    \"qiskit-addon-aqc-tensor[quimb-jax]==0.3.1\",\n",
+    "    \"qiskit-aer==0.17.2\",\n",
+    "    \"cotengrust==0.2.0\",\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "upload-md",
+   "metadata": {},
+   "source": [
+    "## 3. Define and upload the function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "upload-code",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit_ibm_catalog import QiskitFunction\n",
+    "\n",
+    "fn = QiskitFunction(\n",
+    "    title=\"aqc-dynamics-function\",\n",
+    "    entrypoint=\"program.py\",\n",
+    "    working_dir=\"source_files/\",\n",
+    "    dependencies=DEPENDENCIES,\n",
+    ")\n",
+    "serverless.upload(fn)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "verify-md",
+   "metadata": {},
+   "source": [
+    "## 4. Verify it registered"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "verify-code",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "QiskitFunction(aqc-dynamics-function)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(p for p in serverless.list() if p.title == \"aqc-dynamics-function\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "reference-md",
+   "metadata": {},
+   "source": [
+    "## Function reference\n",
+    "\n",
+    "This is a brief introduction. Every field is documented in full in the [AQC Dynamics Template README](https://github.com/qiskit-community/qiskit-function-templates/blob/main/physics/aqc_trotter/README.md): the complete inputs table with its validation rules, the output fields, the execution backends, and further worked examples. What follows is the short version, enough to read the examples below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "inputs-md",
+   "metadata": {},
+   "source": [
+    "### Inputs\n",
+    "\n",
+    "Every run is a single `fn.run(...)` call. Only the first three inputs below are required: `hamiltonian`, `t_steps`, and `aqc_segments`. Everything after them is optional and falls back to the default shown, so a minimal call is three arguments and the rest of the table is the functionality you can opt into. The Hamiltonian's `num_qubits` sets the chain length, so there is no separate size input.\n",
+    "\n",
+    "| Input | Default | Description |\n",
+    "|---|---|---|\n",
+    "| `hamiltonian` | required | 1D nearest-neighbor Pauli Hamiltonian as a `SparsePauliOp`. Strings are Pauli operators, so there is no implicit factor of one half. |\n",
+    "| `t_steps` | required | Total Trotter steps. Evolves to `T = t_steps * dt` and reports every observable at each `t_k = k * dt`. |\n",
+    "| `aqc_segments` | required | Compression plan: a list of `{\"n_steps\": k, \"ansatz_steps\": m}`. `sum(n_steps)` steps are compressed; the rest run as plain Trotter. |\n",
+    "| `dt` | `0.2` | Physical time advanced by one Trotter step. |\n",
+    "| `initial_state` | `\\|0...0>` | A prepared `QuantumCircuit` to evolve. Bake any local kick into this circuit. |\n",
+    "| `observables` | per-site `Z` | Anything `EstimatorV2` accepts as its `observables` argument. One observable per output column. |\n",
+    "| `trotter_options` | 2nd-order Suzuki | `{\"method\": ..., \"synthesis_settings\": {...}}`. `reps` and `time` are owned by the function. |\n",
+    "| `aqc_options` | see below | `max_bond` (`32`), `cutoff` (`1e-8`), `autodiff_backend` (`\"jax\"`), `fidelity_target` (`None`), `optimizer_settings` (L-BFGS-B, `jac=True`, `maxiter=300`). |\n",
+    "| `estimator_options` | DD, twirling, TREX | `EstimatorV2.options`, passed through as-is. A supplied dictionary replaces the defaults wholesale rather than merging into them. |\n",
+    "| `transpiler_options` | `{\"optimization_level\": 3}` | `generate_preset_pass_manager` keyword arguments. `backend` and `target` are rejected, since the execution path owns them. |\n",
+    "| `backend` | `\"runtime\"` | `\"statevector\"`, `\"fake\"`, or `\"runtime\"`. |\n",
+    "| `backend_name` | least busy | IBM backend name for `runtime`, or a named fake backend. |\n",
+    "| `batches` | `1` | Split the circuits across N runtime jobs. One batch submits a single job and creates no session. |\n",
+    "| `parallel_sim` | `False` | Fan the local simulator paths across all available cores with Ray. No effect on `runtime`. |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "backends-md",
+   "metadata": {},
+   "source": [
+    "### Execution backends\n",
+    "\n",
+    "All three paths share the same code and the same mitigation settings. They differ only in where the circuits run.\n",
+    "\n",
+    "| `backend` | What it is | Credentials | Notes |\n",
+    "|---|---|---|---|\n",
+    "| `\"statevector\"` | Exact `StatevectorEstimator` | Serverless account only | The exact reference path. No QPU time. |\n",
+    "| `\"fake\"` | Noisy local simulation on a Qiskit fake backend | Serverless account only | A faithful rehearsal of the mitigated `runtime` path. Needs `qiskit-aer`. Defaults to the 127-qubit `fake_sherbrooke`. |\n",
+    "| `\"runtime\"` (default) | The mitigated `EstimatorV2` against a real QPU | Yes | `backend_name` optional; omitting it selects the least busy device. |\n",
+    "\n",
+    "Both simulator paths still call the deployed function, so they need a saved Serverless account even though they use no QPU time. The two examples below run the same workload on `statevector` first, then on `runtime`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "output-md",
+   "metadata": {},
+   "source": [
+    "### Output\n",
+    "\n",
+    "`job.result()` returns a plain dictionary:\n",
+    "\n",
+    "```python\n",
+    "{\n",
+    "    \"times\": [...],                  # length t_steps + 1, t_k = k * dt (t=0 is the prepared state)\n",
+    "    \"expectation_values\": [[...]],   # shape (n_times, n_observables)\n",
+    "    \"observable_labels\": [...],      # e.g. [\"Z_0\", \"ZZ_0_1\"]\n",
+    "    \"metadata\": {\n",
+    "        \"n\", \"t_steps\", \"dt\", \"tier\",\n",
+    "        \"aqc_compressed_steps\": 5,   # total compressed steps (= sum of segment n_steps)\n",
+    "        \"aqc_segments\": [            # per segment: the plan plus its own results\n",
+    "            {\"n_steps\": 3, \"ansatz_steps\": 1, \"steps\": [1, 2, 3], \"n_params\": 133,\n",
+    "             \"fidelities\": {1: ..., 2: ..., 3: ...}},\n",
+    "            {\"n_steps\": 2, \"ansatz_steps\": 2, \"steps\": [4, 5], \"n_params\": 245,\n",
+    "             \"fidelities\": {4: ..., 5: ...}},\n",
+    "        ],\n",
+    "        \"execution_backend\",\n",
+    "        \"aqc_fidelities\": {1: ..., 2: ...},  # flat per-step fidelity, all compressed steps\n",
+    "        \"circuit_stats\": {           # per-step 2q depth and gate count, full Trotter vs AQC\n",
+    "            1: {\"full_trotter\": {\"depth_2q\": ..., \"num_2q_gates\": ...},\n",
+    "                \"aqc_trotter\":  {\"depth_2q\": ..., \"num_2q_gates\": ...}},\n",
+    "            2: {...},\n",
+    "        },\n",
+    "        \"warnings\": [...],           # non-fatal notices, e.g. a cotengrust fallback\n",
+    "        \"resource_usage\": {          # per stage; QPU_TIME is the charged QPU time\n",
+    "            \"RUNNING: OPTIMIZING_FOR_HARDWARE\": {\"CPU_TIME\": ...},\n",
+    "            \"RUNNING: WAITING_FOR_QPU\": {\"CPU_TIME\": ...},\n",
+    "            \"RUNNING: EXECUTING_QPU\": {\"QPU_TIME\": ...},\n",
+    "        },\n",
+    "    },\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "`aqc_fidelities` and `circuit_stats` are the two to read first: together they tell you whether the compression stayed faithful and whether it actually saved depth. On `runtime`, `resource_usage` reports the queue wait separately from the QPU time you are charged for. A rejected input fails fast as a structured `ServerlessError` (code `4615`)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "sim-md",
+   "metadata": {},
+   "source": [
+    "## Simulator example\n",
+    "\n",
+    "Run the function on the exact `statevector` backend first. It spends no QPU time and validates the deployment end to end. The model here is an 8-qubit transverse-field Ising chain, and `observables` is omitted so the function measures the default per-site $Z$.\n",
+    "\n",
+    "The compression plan is the input worth understanding. Each segment `{\"n_steps\": k, \"ansatz_steps\": m}` compresses `k` consecutive Trotter steps into an ansatz built from an `m`-step Trotter target, and any steps beyond `sum(n_steps)` run as plain Trotter. Early, low-entanglement steps compress well into a shallow 1-layer ansatz; later, more-entangled steps need a deeper one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "sim-code",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "job id: ee1f3793-e995-427d-81d1-5924549beb38\n"
+     ]
+    }
+   ],
+   "source": [
+    "from qiskit.quantum_info import SparsePauliOp\n",
+    "\n",
+    "fn = serverless.load(\"aqc-dynamics-function\")\n",
+    "\n",
+    "n = 8\n",
+    "H = SparsePauliOp.from_sparse_list(\n",
+    "    [(\"ZZ\", [i, i + 1], 1.0) for i in range(n - 1)]\n",
+    "    + [(\"X\", [i], 0.8) for i in range(n)],\n",
+    "    num_qubits=n,\n",
+    ")\n",
+    "\n",
+    "job = fn.run(\n",
+    "    t_steps=8,\n",
+    "    aqc_segments=[\n",
+    "        {\"n_steps\": 4, \"ansatz_steps\": 1},  # early steps -> shallow 1-layer ansatz\n",
+    "        {\"n_steps\": 2, \"ansatz_steps\": 2},  # later steps -> deeper 2-layer ansatz\n",
+    "    ],\n",
+    "    hamiltonian=H,\n",
+    "    aqc_options={\"max_bond\": 32},\n",
+    "    backend=\"statevector\",\n",
+    ")\n",
+    "print(\"job id:\", job.job_id)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "follow-md",
+   "metadata": {},
+   "source": [
+    "### Follow the run and read the result\n",
+    "\n",
+    "`status()` reports both the coarse job lifecycle and the per-stage sub-status the function publishes as it runs. The same stages apply to the hardware run below:\n",
+    "\n",
+    "`QUEUED -> INITIALIZING -> RUNNING: OPTIMIZING_FOR_HARDWARE -> RUNNING: WAITING_FOR_QPU -> RUNNING: EXECUTING_QPU -> RUNNING: POST_PROCESSING -> DONE`\n",
+    "\n",
+    "| `status()` value | Stage |\n",
+    "|---|---|\n",
+    "| `RUNNING: OPTIMIZING_FOR_HARDWARE` | state prep, Trotter build, AQC compression |\n",
+    "| `RUNNING: WAITING_FOR_QPU` | queued on the QPU (`runtime` backend only) |\n",
+    "| `RUNNING: EXECUTING_QPU` | circuits executing (local sims mark this directly) |\n",
+    "| `RUNNING: POST_PROCESSING` | assembling the result dictionary |\n",
+    "\n",
+    "Terminal states are `DONE`, `ERROR`, and `CANCELED`. This `statevector` run has no QPU queue, so it skips `RUNNING: WAITING_FOR_QPU`. Use `job.logs()` at any point to see the per-stage logs, including the AQC fidelity reached at each step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "status-code",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DONE\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(job.status())  # re-run until this reports DONE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "result-code",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "observable labels: ['Z_0', 'Z_1', 'Z_2', 'Z_3', 'Z_4', 'Z_5', 'Z_6', 'Z_7']\n",
+      "times: [0.0, 0.2, 0.4, 0.6000000000000001, 0.8, 1.0, 1.2000000000000002, 1.4000000000000001, 1.6]\n",
+      "AQC fidelities: {'1': 1.0, '2': 1.0, '3': 1.0, '4': 1.0, '5': 1.0, '6': 0.9999}\n"
+     ]
+    }
+   ],
+   "source": [
+    "result = job.result()\n",
+    "print(\"observable labels:\", result[\"observable_labels\"])\n",
+    "print(\"times:\", result[\"times\"])\n",
+    "print(\n",
+    "    \"AQC fidelities:\",\n",
+    "    {k: round(v, 4) for k, v in result[\"metadata\"][\"aqc_fidelities\"].items()},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "hw-md",
+   "metadata": {},
+   "source": [
+    "## Hardware example\n",
+    "\n",
+    "A function call with `backend=\"runtime\"` transpiles and executes on a real IBM Quantum processor, with the function's built-in error mitigation: dynamical decoupling (XY4), gate twirling, and twirled readout error extinction (TREX). `backend_name` selects the device; omit it and the function takes the least busy one.\n",
+    "\n",
+    "Nothing about the science code changes. Only the chain length, the number of Trotter steps, and the backend differ from the simulator example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "hw-code",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit.quantum_info import SparsePauliOp\n",
+    "\n",
+    "fn = serverless.load(\"aqc-dynamics-function\")\n",
+    "\n",
+    "n = 10\n",
+    "H = SparsePauliOp.from_sparse_list(\n",
+    "    [(\"ZZ\", [i, i + 1], 1.0) for i in range(n - 1)]\n",
+    "    + [(\"X\", [i], 0.8) for i in range(n)],\n",
+    "    num_qubits=n,\n",
+    ")\n",
+    "\n",
+    "job = fn.run(\n",
+    "    t_steps=10,\n",
+    "    aqc_segments=[\n",
+    "        {\"n_steps\": 3, \"ansatz_steps\": 1},\n",
+    "        {\"n_steps\": 3, \"ansatz_steps\": 2},\n",
+    "    ],\n",
+    "    hamiltonian=H,\n",
+    "    backend=\"runtime\",\n",
+    "    backend_name=\"ibm_boston\",\n",
+    ")\n",
+    "print(\"job id (save this to reconnect later):\", job.job_id)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "hw-reconnect-md",
+   "metadata": {},
+   "source": [
+    "<Admonition type=\"note\" title=\"Reconnecting to a long-running job\">\n",
+    "\n",
+    "A hardware run is not quick, and most of the time is classical rather than on the QPU. The AQC compression runs inside the function before anything reaches the QPU, and the QPU queue is on top of that. You do not need to keep this notebook or kernel open while it runs.\n",
+    "\n",
+    "Copy the job id printed above and save it. The next three cells let you pick the run back up later:\n",
+    "\n",
+    "1. Reconnect, only needed in a new kernel session: re-run the [Authentication](#1-authentication) cell to recreate `serverless`, then rebuild the `job` handle from the id you saved. Skip this cell if you are still in the session where you submitted, because the handle is already live.\n",
+    "2. Check status: re-run until it reports `DONE`.\n",
+    "3. Fetch the result: run only once the status is `DONE`.\n",
+    "\n",
+    "Paste your saved id over the placeholder in the reconnect cell below.\n",
+    "\n",
+    "</Admonition>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "hw-reconnect",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Reconnect to a previously submitted job by its id. Only needed in a NEW kernel\n",
+    "# session; if you are still in the session where you submitted, the `job` handle\n",
+    "# above is already live, so skip this cell. Replace the id below with your own.\n",
+    "job = serverless.get_job_by_id(\"<your job id>\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "hw-status",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Re-run this until it reports DONE, then fetch the result below.\n",
+    "print(job.status())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "hw-result-code",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Run this only once the status cell above reports DONE. result() blocks until\n",
+    "# the job finishes, so calling it earlier just waits.\n",
+    "result = job.result()\n",
+    "print(\"execution backend:\", result[\"metadata\"][\"execution_backend\"])\n",
+    "print(\n",
+    "    \"AQC fidelities:\",\n",
+    "    {k: round(v, 4) for k, v in result[\"metadata\"][\"aqc_fidelities\"].items()},\n",
+    ")\n",
+    "print(\"resource usage:\", result[\"metadata\"][\"resource_usage\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "nextsteps",
+   "metadata": {},
+   "source": [
+    "## Next steps\n",
+    "\n",
+    "<Admonition type=\"tip\" title=\"Recommendations\">\n",
+    "\n",
+    "- Work through [Simulate neutron scattering with an AQC-dynamics Qiskit Function](neutron-scattering-with-qiskit-functions.ipynb), the companion example that calls this deployed function to compute the dynamical structure factor of KCuF$_3$.\n",
+    "- Read the [AQC Dynamics Function Template Github](https://github.com/qiskit-community/qiskit-function-templates/blob/main/physics/aqc_trotter/) for the complete input and output contract, further examples, and citation details.\n",
+    "- Browse the [Qiskit function templates repository](https://github.com/qiskit-community/qiskit-function-templates/tree/main/physics/aqc_trotter) for other application templates built the same way.\n",
+    "- Read the [Qiskit Serverless guide](https://quantum.cloud.ibm.com/docs/en/guides/serverless) for managing deployed functions.\n",
+    "- Go deeper on the AQC compression stage with the [Qiskit addon: AQC-Tensor](https://qiskit.github.io/qiskit-addon-aqc-tensor/) documentation.\n",
+    "\n",
+    "</Admonition>"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "neutron-scattering",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function.ipynb b/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function.ipynb
new file mode 100644
index 00000000000..f38200c2914
--- /dev/null
+++ b/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function.ipynb
@@ -0,0 +1,885 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "frontmatter",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "title: Simulate neutron scattering with an AQC-dynamics Qiskit Function\n",
+    "description: Compute the dynamical structure factor S(q, w) of the quantum magnet KCuF3 by running an AQC-compressed Trotter workflow as a Qiskit Function.\n",
+    "---\n",
+    "\n",
+    "{/* cspell:ignore Trotter Trotterization spinon spinons KCuF quimb DMRG magnon antiferromagnetic Suzuki fidelities isa COBYQA */}\n",
+    "\n",
+    "# Simulate neutron scattering with an AQC-dynamics Qiskit Function\n",
+    "*Usage estimate: 18 minutes on a Heron r3 processor (NOTE: This is an estimate only. Your runtime might vary.)*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "learning-outcomes",
+   "metadata": {},
+   "source": [
+    "## Learning outcomes\n",
+    "\n",
+    "After completing this tutorial, you can expect to understand:\n",
+    "\n",
+    "- How an inelastic neutron-scattering spectrum maps to the dynamical structure factor $S(q, \\omega)$ of a 1D quantum magnet.\n",
+    "- How to prepare the KCuF$_3$ (isotropic Heisenberg) ground state with the density matrix renormalization group (DMRG) and matrix product state (MPS) fidelity maximization.\n",
+    "- How to run Trotter time-evolution, approximate quantum compilation (AQC) circuit compression, and mitigated execution as a single Qiskit Function call.\n",
+    "- How to post-process the per-site $\\langle \\sigma_z \\rangle(t)$ time series into $S(q, \\omega)$ and identify the two-spinon continuum."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "prerequisites",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "\n",
+    "- Familiarity with [Qiskit Patterns](/docs/guides/intro-to-patterns), [`SparsePauliOp`](/docs/api/qiskit/qiskit.quantum_info.SparsePauliOp), and [Trotter time-evolution](/learning/courses/utility-scale-quantum-computing/quantum-simulation).\n",
+    "- Basic exposure to tensor-network methods (DMRG and MPS) is helpful but not required, as is familiarity with the [`qiskit-addon-aqc-tensor`](https://github.com/Qiskit/qiskit-addon-aqc-tensor) library that the function uses to compress Trotter circuits."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "background",
+   "metadata": {},
+   "source": [
+    "## Background\n",
+    "\n",
+    "Inelastic neutron scattering measures the dynamical structure factor $S(q, \\omega)$, the space-and-time Fourier transform of the spin-spin correlation function, so reproducing $S(q, \\omega)$ from a microscopic spin model is a direct, falsifiable test of a quantum simulation. This tutorial studies KCuF$_3$, a spin-$\\frac{1}{2}$ antiferromagnetic Heisenberg chain whose excitations are not single spin flips but pairs of fractionalized spinons: instead of a sharp magnon dispersion, $S(q, \\omega)$ shows a broad *two-spinon continuum*, bounded below by $\\tfrac{\\pi}{2}|\\sin q|$ and above by $\\pi|\\sin(q/2)|$. Those are the dashed curves on the plots below. The physics in full, and the comparison against measured neutron data, are covered in the [original tutorial](/docs/tutorials/simulate-neutron-scattering) and in Lee et al., [arXiv:2603.15608](https://arxiv.org/abs/2603.15608).\n",
+    "\n",
+    "The quantum workflow mirrors the scattering experiment:\n",
+    "\n",
+    "1. Prepare the chain's ground state $|\\psi_0\\rangle$.\n",
+    "2. Kick it with a local perturbation at the center site, a $\\pi/2$ $Z$-rotation, mimicking the momentum and energy transfer from the neutron.\n",
+    "3. Time-evolve under the Heisenberg Hamiltonian, $e^{-iHt}$, with a Trotter product formula.\n",
+    "4. Measure the per-site magnetization $\\langle \\sigma_z^j \\rangle(t)$; as a function of site $j$ and time $t$ this *is* the retarded Green's function $G^R(j, j_c, t)$.\n",
+    "5. Fourier transform $G^R$ into $S(q, \\omega)$.\n",
+    "\n",
+    "The bottleneck is step 3: exact Trotter circuits for long evolutions become too deep for hardware. Approximate quantum compilation with tensor networks (AQC) addresses this by compressing a block of Trotter steps into a fixed, shallow parameterized ansatz whose state fidelity to the exact evolution is maximized classically with an MPS simulator ([arXiv:2301.08609](https://arxiv.org/abs/2301.08609)). The [AQC Dynamics Function](/docs/guides/function-template-aqc-trotter) packages this whole quantum core (Trotter synthesis, AQC compression, and mitigated execution) behind one call:\n",
+    "\n",
+    "```\n",
+    "   PRE (this notebook)          FUNCTION (aqc-dynamics-function)     POST (this notebook)\n",
+    " ground state (DMRG + MPS    ->  Trotter -> AQC compress -> execute -> S(q, w): the dynamical\n",
+    " fidelity max) + neutron kick    (statevector / fake / runtime)        structure factor\n",
+    "                                 -> <sigma_z>(t) per site\n",
+    "```\n",
+    "\n",
+    "So the experiment-specific work stays here in the notebook: ground-state preparation (PRE) and the $S(q, \\omega)$ post-processing (POST). The two quantum-heavy steps, compression and execution, run inside the function.\n",
+    "\n",
+    "This tutorial is a companion to [Simulate neutron scattering in quantum materials with quantum circuits](/docs/tutorials/simulate-neutron-scattering), which builds the same experiment inline: the same KCuF$_3$ model, ground-state preparation, neutron kick, and post-processing, with the Trotter synthesis, AQC compression, and mitigated execution written out step by step. Read that tutorial to learn how AQC compression works. Read this one to run the same experiment through a deployed Qiskit Function: the quantum core becomes a single function call, and the multi-hour AQC compression runs inside the Serverless worker instead of on your machine, so you do not need an HPC system or an open kernel while it runs. Because the function is Hamiltonian-agnostic, the same call also drives other dynamics experiments."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "requirements",
+   "metadata": {},
+   "source": [
+    "## Requirements\n",
+    "\n",
+    "Before starting this tutorial, be sure you have the following:\n",
+    "\n",
+    "- The function deployed to your IBM Quantum&reg; Serverless account. Run the companion function template first: [Deploy and run the AQC dynamics Qiskit Function](/docs/guides/function-template-aqc-trotter). That guide walks through getting the source files and uploading the function to your account. This tutorial only calls the deployed function.\n",
+    "\n",
+    "- IBM Quantum credentials saved for `QiskitServerless` (see the function template). Both examples below call the deployed function, so both need them.\n",
+    "\n",
+    "- Qiskit SDK v2.0 or later (`pip install qiskit`).\n",
+    "\n",
+    "- The Qiskit IBM Catalog client (`pip install qiskit-ibm-catalog`).\n",
+    "\n",
+    "- NumPy, SciPy, and Matplotlib (`pip install numpy scipy matplotlib`). SciPy 1.14 or later is needed for the COBYQA optimizer used in ground-state preparation.\n",
+    "\n",
+    "- The AQC tensor-network stack, because the ground-state preparation in Step 1 runs locally in this notebook: `pip install 'qiskit-addon-aqc-tensor[quimb-jax]==0.3.1'`\n",
+    "\n",
+    "\n",
+    "The first call to a newly deployed function waits while the Serverless worker installs its dependencies, so expect extra latency on that run."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "setup-md",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "Import the libraries and define the experiment-specific helpers used below: `build_gs_ansatz` (the Hamiltonian variational ansatz, or HVA, for ground-state preparation), `prepare_ground_state` (DMRG plus MPS-fidelity maximization), and `get_spectrum`, `plot_green`, and `plot_spectrum` (the $S(q, \\omega)$ post-processing). These are adapted from the [original neutron-scattering tutorial](/docs/tutorials/simulate-neutron-scattering)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "setup-imports",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from functools import partial\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import scipy.optimize\n",
+    "\n",
+    "import quimb.tensor as qtn\n",
+    "from qiskit import QuantumCircuit\n",
+    "from qiskit.quantum_info import SparsePauliOp\n",
+    "from qiskit_addon_aqc_tensor.simulation import tensornetwork_from_circuit\n",
+    "from qiskit_addon_aqc_tensor.simulation.quimb import QuimbSimulator\n",
+    "from qiskit_ibm_catalog import QiskitServerless"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "setup-helpers",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Setup complete - helpers defined.\n"
+     ]
+    }
+   ],
+   "source": [
+    "#  Dynamical structure factor via discrete Fourier transform\n",
+    "\n",
+    "\n",
+    "def get_spectrum(n, Gjjc, dt, time_steps, q_steps, w_steps):\n",
+    "    \"\"\"Compute the dynamical structure factor from the retarded Green's function.\n",
+    "\n",
+    "    Uses the center-site approximation and a discrete Fourier transform.\n",
+    "    \"\"\"\n",
+    "    green = Gjjc / 4  # sigma -> S=1/2\n",
+    "    omega_max = np.pi / dt\n",
+    "    qpoints = np.arange(0, 2 * np.pi, 2 * np.pi / q_steps)\n",
+    "    omegas = np.arange(0, omega_max, omega_max / w_steps)\n",
+    "    green_map = np.zeros((omegas.shape[0], qpoints.shape[0]))\n",
+    "    center = n // 2 - 1\n",
+    "    for iw, w in enumerate(omegas):\n",
+    "        exponent = np.exp(1j * w * dt * np.arange(1, time_steps + 1))\n",
+    "        S_w = np.dot(green.T, exponent) * dt\n",
+    "        for iq, q in enumerate(qpoints):\n",
+    "            q_matrix = np.exp(-1j * q * np.arange(-center, center + 2, 1))\n",
+    "            green_map[iw, iq] = np.imag(np.dot(S_w, q_matrix))\n",
+    "    return green_map\n",
+    "\n",
+    "\n",
+    "#  Plotting helpers\n",
+    "\n",
+    "\n",
+    "def plot_spectrum(\n",
+    "    dsf, dt, q_steps, w_steps, lower_bound=False, upper_bound=False, title=None\n",
+    "):\n",
+    "    \"\"\"Heat-map of the dynamical structure factor.\"\"\"\n",
+    "    omega_max = np.pi / dt\n",
+    "    qpoints = np.arange(0, 2 * np.pi, 2 * np.pi / q_steps)\n",
+    "    omegas = np.arange(0, omega_max, omega_max / w_steps)\n",
+    "    x, y = np.meshgrid(qpoints, omegas)\n",
+    "    fig, ax = plt.subplots(figsize=(8, 5))\n",
+    "    c = ax.pcolormesh(x, y, dsf / np.max(dsf), cmap=\"viridis\", shading=\"auto\")\n",
+    "    fig.colorbar(c, ax=ax, label=\"Normalized intensity\")\n",
+    "    if lower_bound:\n",
+    "        ax.plot(\n",
+    "            qpoints,\n",
+    "            np.pi * np.abs(np.sin(qpoints)) / 2,\n",
+    "            \"--\",\n",
+    "            color=\"white\",\n",
+    "            lw=1.5,\n",
+    "            label=\"Lower bound\",\n",
+    "        )\n",
+    "    if upper_bound:\n",
+    "        ax.plot(\n",
+    "            qpoints,\n",
+    "            np.pi * np.abs(np.sin(qpoints / 2)),\n",
+    "            \"--\",\n",
+    "            color=\"red\",\n",
+    "            lw=1.5,\n",
+    "            label=\"Upper bound\",\n",
+    "        )\n",
+    "    ax.set_ylim(0, 3.6)\n",
+    "    ax.set_xlim(0, 2 * np.pi - 2 * np.pi / q_steps)\n",
+    "    ax.set_xlabel(r\"$q$\", fontsize=16)\n",
+    "    ax.set_ylabel(r\"$\\tilde{\\omega} = \\omega / J$\", fontsize=16)\n",
+    "    ax.set_xticks([0, np.pi / 2, np.pi, 3 * np.pi / 2, 2 * np.pi])\n",
+    "    ax.set_xticklabels([\"0\", r\"$\\pi/2$\", r\"$\\pi$\", r\"$3\\pi/2$\", r\"$2\\pi$\"])\n",
+    "    if lower_bound or upper_bound:\n",
+    "        ax.legend(loc=\"upper right\", fontsize=11)\n",
+    "    if title:\n",
+    "        ax.set_title(title, fontsize=14)\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "def plot_green(n, Gjjc, time_steps, dt, title=None):\n",
+    "    \"\"\"Heat-map of the retarded Green's function in real space and time.\"\"\"\n",
+    "    fig, ax = plt.subplots(figsize=(8, 6))\n",
+    "    t_axis = np.arange(1, time_steps + 1) * dt\n",
+    "    site_axis = np.arange(n)\n",
+    "    x, y = np.meshgrid(t_axis, site_axis)\n",
+    "    c = ax.pcolormesh(\n",
+    "        x, y, np.real(Gjjc).T, cmap=\"RdBu\", vmax=0.5, vmin=-0.5, shading=\"auto\"\n",
+    "    )\n",
+    "    fig.colorbar(c, ax=ax, label=r\"Re $G^R(j, j_c, t)$\")\n",
+    "    ax.set_xlabel(r\"Time  ($t / J^{-1}$)\", fontsize=16)\n",
+    "    ax.set_ylabel(\"Site index $j$\", fontsize=16)\n",
+    "    if title:\n",
+    "        ax.set_title(title, fontsize=14)\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "#  Variational ground-state ansatz (HVA)\n",
+    "\n",
+    "\n",
+    "def _apply_xxz_pair_gate(qc, q0, q1, theta):\n",
+    "    \"\"\"Apply the parameterized XXZ-type two-qubit gate used in the HVA.\"\"\"\n",
+    "    qc.cx(q0, q1)\n",
+    "    qc.rz(theta, q1)\n",
+    "    qc.h(q0)\n",
+    "    qc.rz(theta + np.pi / 2, q0)\n",
+    "    qc.cx(q0, q1)\n",
+    "    qc.rz(-theta, q1)\n",
+    "    qc.h(q1)\n",
+    "    qc.cx(q1, q0)\n",
+    "    qc.rz(np.pi / 2, q1)\n",
+    "    qc.rz(-np.pi / 2, q0)\n",
+    "    qc.h(q1)\n",
+    "    qc.h(q0)\n",
+    "\n",
+    "\n",
+    "def build_gs_ansatz(n, params, layers):\n",
+    "    \"\"\"Build the Hamiltonian variational ansatz (HVA) circuit for\n",
+    "    ground-state preparation of the 1D Heisenberg model.\n",
+    "\n",
+    "    Starts from a product of singlet pairs and applies alternating\n",
+    "    odd/even layers of parameterized XXZ gates. For layer r,\n",
+    "    params[2 * r] is the odd-layer (inter-pair) angle and\n",
+    "    params[2 * r + 1] is the even-layer (intra-pair) angle.\n",
+    "    \"\"\"\n",
+    "    qc = QuantumCircuit(n)\n",
+    "    # Initial singlet product state\n",
+    "    for i in range(n // 2):\n",
+    "        qc.x(2 * i)\n",
+    "        qc.x(2 * i + 1)\n",
+    "        qc.h(2 * i + 1)\n",
+    "        qc.cx(2 * i + 1, 2 * i)\n",
+    "    # Variational layers\n",
+    "    for r in range(layers):\n",
+    "        for i in range(1, (n + 1) // 2):  # odd layer\n",
+    "            _apply_xxz_pair_gate(qc, 2 * i - 1, 2 * i, params[2 * r])\n",
+    "        for i in range(n // 2):  # even layer\n",
+    "            _apply_xxz_pair_gate(qc, 2 * i, 2 * i + 1, params[2 * r + 1])\n",
+    "    return qc\n",
+    "\n",
+    "\n",
+    "def prepare_ground_state(n, gs_layers=5, max_bond=128, cutoff=1e-8):\n",
+    "    \"\"\"Prepare the KCuF3 (isotropic Heisenberg) ground state as a QuantumCircuit.\n",
+    "\n",
+    "    Runs DMRG (quimb MPO + DMRG2) to get the chain's ground state, then optimizes\n",
+    "    the HVA angles to maximize the MPS overlap |<psi_ansatz|psi_DMRG>|^2. No exact\n",
+    "    diagonalization, so it scales to larger n.\n",
+    "    \"\"\"\n",
+    "    J = Jz = 1.0\n",
+    "    builder = qtn.SpinHam1D(S=1 / 2)\n",
+    "    builder += J * 0.5, \"+\", \"-\"\n",
+    "    builder += J * 0.5, \"-\", \"+\"\n",
+    "    builder += Jz, \"Z\", \"Z\"\n",
+    "    H_mpo = builder.build_mpo(L=n)\n",
+    "    dmrg = qtn.DMRG2(H_mpo)\n",
+    "    dmrg.solve(tol=1e-8, verbosity=0)\n",
+    "\n",
+    "    gs_sim = QuimbSimulator(\n",
+    "        quimb_circuit_factory=partial(\n",
+    "            qtn.CircuitMPS, gate_opts=dict(cutoff=cutoff, max_bond=max_bond)\n",
+    "        ),\n",
+    "        autodiff_backend=\"jax\",\n",
+    "    )\n",
+    "\n",
+    "    def gs_infidelity(params):\n",
+    "        psi = tensornetwork_from_circuit(\n",
+    "            build_gs_ansatz(n, params, gs_layers), gs_sim\n",
+    "        ).psi\n",
+    "        return 1 - abs(psi.H @ dmrg.state) ** 2\n",
+    "\n",
+    "    # Seed and optimizer match the original tutorial. Each layer starts at\n",
+    "    # [0, pi/2]: an odd-layer angle of 0 makes the inter-pair gate the identity,\n",
+    "    # and an even-layer angle of pi/2 makes the intra-pair gate a SWAP (since\n",
+    "    # 0.5 * (XX + YY + ZZ) = SWAP - I/2). That puts the seed at the singlet-pair\n",
+    "    # product limit, which is already a decent approximation to the Heisenberg\n",
+    "    # ground state, so the optimizer only has to refine it. The small jitter\n",
+    "    # (fixed RNG seed, so runs are reproducible) breaks the exact symmetry\n",
+    "    # between layers; COBYQA then runs for up to 100 iterations.\n",
+    "    rng = np.random.default_rng(12345)\n",
+    "    x0 = np.tile([0.0, np.pi / 2], gs_layers) + rng.normal(\n",
+    "        scale=0.1, size=2 * gs_layers\n",
+    "    )\n",
+    "    result_gs = scipy.optimize.minimize(\n",
+    "        gs_infidelity, x0, method=\"COBYQA\", options={\"maxiter\": 100}\n",
+    "    )\n",
+    "    print(f\"DMRG ground-state energy: {dmrg.energy:.6f}\")\n",
+    "    print(f\"GS fidelity: {1 - result_gs.fun:.4f}\")\n",
+    "    return build_gs_ansatz(n, result_gs.x, gs_layers)\n",
+    "\n",
+    "\n",
+    "print(\"Setup complete - helpers defined.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "setup-load-md",
+   "metadata": {},
+   "source": [
+    "### Load the Qiskit Function\n",
+    "\n",
+    "Connect to IBM Quantum Serverless and load the deployed `aqc-dynamics-function`. Both examples below call the same `fn` handle, so the function is loaded once, here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "setup-connect",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Credentials are read from the account saved once via QiskitServerless.save_account(...)\n",
+    "serverless = QiskitServerless()\n",
+    "fn = serverless.load(\"aqc-dynamics-function\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "small-md",
+   "metadata": {},
+   "source": [
+    "## Small-scale simulator example\n",
+    "\n",
+    "We first run the full workflow on a small 10-site chain using the exact `statevector` backend. This validates the PRE &rarr; FUNCTION &rarr; POST pipeline before spending any QPU time."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "small-s1-md",
+   "metadata": {},
+   "source": [
+    "### Step 1: Map classical inputs to a quantum problem\n",
+    "\n",
+    "Build the KCuF$_3$ Hamiltonian as a `SparsePauliOp` (isotropic Heisenberg: $XX + YY + ZZ$ at coupling $\\tfrac14$ on each nearest-neighbor bond; the strings are Pauli operators, so $\\tfrac14$ gives the spin-$\\frac{1}{2}$ coupling). Prepare the ground state with DMRG plus MPS-fidelity maximization, then bake in the neutron kick: a $\\pi/2$ $Z$-rotation at the center site. The prepared circuit is what we hand to the function as `initial_state`. We leave `observables` at its default (per-site $Z$), which is exactly the $\\langle \\sigma_z^j \\rangle(t)$ readout the neutron workflow needs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "small-s1-code",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DMRG ground-state energy: -4.258035\n",
+      "GS fidelity: 0.9841\n",
+      "Prepared 10-qubit ground state with the neutron kick at site 4.\n"
+     ]
+    }
+   ],
+   "source": [
+    "n = 10\n",
+    "dt = 0.6  # physical time per Trotter step (also the omega-axis unit in POST)\n",
+    "time_steps = 10\n",
+    "center = n // 2 - 1\n",
+    "\n",
+    "# MPS-simulator settings, shared by the ground-state prep here and the AQC\n",
+    "# compression inside the function (matches the original tutorial).\n",
+    "mps_max_bond = 32\n",
+    "mps_cutoff = 1e-8\n",
+    "\n",
+    "# 1D isotropic Heisenberg (KCuF3) Hamiltonian on n qubits\n",
+    "H = SparsePauliOp.from_sparse_list(\n",
+    "    [(p, [i, i + 1], 0.25) for i in range(n - 1) for p in (\"XX\", \"YY\", \"ZZ\")],\n",
+    "    num_qubits=n,\n",
+    ")\n",
+    "\n",
+    "# Ground state (DMRG + fidelity max) + neutron kick baked into the same circuit\n",
+    "gs_circuit = prepare_ground_state(\n",
+    "    n, gs_layers=3, max_bond=mps_max_bond, cutoff=mps_cutoff\n",
+    ")\n",
+    "gs_circuit.rz(np.pi / 2, center)  # exp(-i (pi/2)/2 Z_center): the neutron perturbation\n",
+    "print(f\"Prepared {n}-qubit ground state with the neutron kick at site {center}.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "small-s3-md",
+   "metadata": {},
+   "source": [
+    "### Steps 2 and 3: Compress and execute with the Qiskit Function\n",
+    "\n",
+    "In a hand-written workflow these are two separate stages: optimize the circuits for hardware (Step 2) and execute them (Step 3). The Qiskit Function collapses both into one call. It performs Trotter synthesis, AQC compression, and hardware transpilation, then runs the circuits (here on the exact simulator, later with built-in error mitigation on hardware). The two tuning parameters are `aqc_segments` (the compression plan) and `aqc_options` (the MPS and optimizer settings). Each segment `{\"n_steps\": k, \"ansatz_steps\": m}` compresses `k` consecutive Trotter steps into an ansatz built from an `m`-step Trotter target, and any steps beyond `sum(n_steps)` run as plain Trotter. Early, low-entanglement steps compress well into a shallow (`ansatz_steps=1`) ansatz, so here we compress the first 3 steps into a 1-layer ansatz and the next 2 into a deeper 2-layer ansatz; the remaining 5 of the 10 Trotter steps run as plain Trotter. For `aqc_options` we mirror the original tutorial: MPS bond dimension `max_bond=32`, `cutoff=1e-8`, and an L-BFGS-B optimizer capped at 100 iterations.\n",
+    "\n",
+    "Call the function loaded in Setup. `backend=\"statevector\"` runs the exact reference path: no QPU time, with the circuits running on an exact statevector simulator inside the serverless worker (a saved Serverless account is still needed to call it). The `initial_state` carries the prepared ground state (including the kick); `observables` is omitted so the function measures the default per-site $Z$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "small-s3-run",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "job = fn.run(\n",
+    "    t_steps=time_steps,\n",
+    "    aqc_segments=[\n",
+    "        {\"n_steps\": 3, \"ansatz_steps\": 1},  # early steps -> shallow 1-layer ansatz\n",
+    "        {\"n_steps\": 2, \"ansatz_steps\": 2},  # later steps -> deeper 2-layer ansatz\n",
+    "    ],\n",
+    "    aqc_options={\n",
+    "        \"max_bond\": mps_max_bond,  # MPS bond dimension for AQC compression\n",
+    "        \"cutoff\": mps_cutoff,\n",
+    "        \"optimizer_settings\": {\n",
+    "            \"method\": \"L-BFGS-B\",\n",
+    "            \"jac\": True,\n",
+    "            \"options\": {\"maxiter\": 100},\n",
+    "        },\n",
+    "    },\n",
+    "    dt=dt,\n",
+    "    hamiltonian=H,\n",
+    "    initial_state=gs_circuit,  # prepared ground state including the neutron kick\n",
+    "    # observables omitted -> default per-site Z (the neutron sigma_z readout)\n",
+    "    backend=\"statevector\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "bf76d298",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DONE\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(job.status())  # rerun this cell until status says DONE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "small-s3-result",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AQC fidelities: {'1': 1.0, '2': 0.9999, '3': 0.9992, '4': 0.9998, '5': 0.9995}\n",
+      "Green's function shape: (10, 10)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# The per-site <sigma_z>(t) the function returns is the retarded Green's function\n",
+    "# G(j, j_c, t). The workflow samples t = 1..time_steps, so drop the t = 0 row (the\n",
+    "# prepared+kicked state before any evolution) before post-processing.\n",
+    "result = job.result()\n",
+    "print(\n",
+    "    \"AQC fidelities:\",\n",
+    "    {k: round(v, 4) for k, v in result[\"metadata\"][\"aqc_fidelities\"].items()},\n",
+    ")\n",
+    "\n",
+    "ev = np.array(result[\"expectation_values\"])\n",
+    "Gjjc = ev[1:]  # shape (time_steps, n)\n",
+    "print(\"Green's function shape:\", Gjjc.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "small-s4-md",
+   "metadata": {},
+   "source": [
+    "### Step 4: Post-process and return result in desired classical format\n",
+    "\n",
+    "Fourier-transform the Green's function into $S(q, \\omega)$, mirror-symmetrize, and clip negatives: the standard neutron post-processing. Mirroring is exact because $S(q, \\omega) = S(-q, \\omega)$ for this model, and the negative values that survive are artifacts of Fourier-transforming a finite, discretely sampled time series, so they are clipped to zero. On this small exact run the two-spinon continuum is only coarsely resolved, but the machinery is identical to the hardware run below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "small-s4-code",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 800x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "q_res, w_res = 100, 100\n",
+    "spectrum = get_spectrum(n, Gjjc, dt, time_steps, q_res, w_res)\n",
+    "spectrum = -(spectrum + spectrum[:, ::-1]) / 2  # mirror symmetry\n",
+    "spectrum = np.clip(spectrum, a_min=0, a_max=None)  # clip negatives\n",
+    "\n",
+    "plot_green(\n",
+    "    n,\n",
+    "    Gjjc,\n",
+    "    time_steps,\n",
+    "    dt,\n",
+    "    title=f\"Retarded Green's function - {n} qubits (AQC, statevector)\",\n",
+    ")\n",
+    "plot_spectrum(\n",
+    "    spectrum,\n",
+    "    dt,\n",
+    "    q_res,\n",
+    "    w_res,\n",
+    "    lower_bound=True,\n",
+    "    upper_bound=True,\n",
+    "    title=f\"Dynamical structure factor - {n} qubits (AQC, statevector)\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "large-md",
+   "metadata": {},
+   "source": [
+    "## Large-scale hardware example\n",
+    "\n",
+    "The same workflow scales up without changing any of the science code: a 30-site chain, twice the Trotter depth (20 steps), a compression plan that varies the ansatz depth (a deeper ansatz for the later, more-entangled steps), and execution on a real IBM Quantum processor with the function's built-in error mitigation (dynamical decoupling, Pauli twirling, and twirled readout error extinction, or TREX). We walk through the same four steps as the simulator example, reusing the `fn` handle from Setup.\n",
+    "\n",
+    "|                                  | Small scale   | Large scale                           |\n",
+    "| -------------------------------- | ------------- | ------------------------------------- |\n",
+    "| Qubits                           | 10            | 30                                    |\n",
+    "| Trotter steps                    | 10            | 20                                    |\n",
+    "| AQC segments (1-layer + 2-layer) | 3 + 2 = 5     | 6 + 4 = 10                            |\n",
+    "| Ground-state ansatz layers       | 3             | 5                                     |\n",
+    "| MPS max bond dimension           | 32            | 128                                   |\n",
+    "| Backend                          | `statevector` | QPU with DD, Pauli twirling, and TREX |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "large-s1-md",
+   "metadata": {},
+   "source": [
+    "### Step 1: Map classical inputs to a quantum problem\n",
+    "\n",
+    "Build the same KCuF$_3$ Heisenberg `SparsePauliOp` and prepare the ground state, now with a deeper `gs_layers=5` ansatz for the longer chain, then bake in the $\\pi/2$ $Z$ neutron kick at the center site. This is identical to the small-scale mapping, just at $n = 30$.\n",
+    "\n",
+    "Expect a lower ground-state fidelity than the 10-site run: around 0.82 here against 0.98 above, because five HVA layers cannot fully capture a 30-site ground state. That is expected rather than a failure, and the original tutorial accepts roughly 0.65 at 50 sites for the same reason. Raising `gs_layers` or the COBYQA iteration cap improves it, at extra classical cost."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "large-code",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DMRG ground-state energy: -13.111355\n",
+      "GS fidelity: 0.8201\n",
+      "Prepared 30-qubit ground state with the neutron kick at site 14.\n"
+     ]
+    }
+   ],
+   "source": [
+    "n = 30\n",
+    "dt = 0.6\n",
+    "time_steps = 20\n",
+    "center = n // 2 - 1\n",
+    "\n",
+    "# Same MPS settings as the original large-scale run: a larger bond for the\n",
+    "# longer, more-entangled chain (shared by GS prep and AQC compression).\n",
+    "mps_max_bond = 128\n",
+    "mps_cutoff = 1e-8\n",
+    "\n",
+    "# Same KCuF3 Hamiltonian and ground-state prep, on a larger chain\n",
+    "H = SparsePauliOp.from_sparse_list(\n",
+    "    [(p, [i, i + 1], 0.25) for i in range(n - 1) for p in (\"XX\", \"YY\", \"ZZ\")],\n",
+    "    num_qubits=n,\n",
+    ")\n",
+    "gs_circuit = prepare_ground_state(\n",
+    "    n, gs_layers=5, max_bond=mps_max_bond, cutoff=mps_cutoff\n",
+    ")\n",
+    "gs_circuit.rz(np.pi / 2, center)  # neutron kick at the center site\n",
+    "print(f\"Prepared {n}-qubit ground state with the neutron kick at site {center}.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "large-s3-md",
+   "metadata": {},
+   "source": [
+    "### Steps 2 and 3: Compress and execute with the Qiskit Function\n",
+    "\n",
+    "The same single call as the simulator example, now with `backend_name` pointing at a real IBM Quantum processor, so the function transpiles and executes there. The compression plan varies the ansatz depth: the first 6 (low-entanglement) Trotter steps compress into a shallow 1-layer ansatz, the next 4 into a deeper 2-layer ansatz, and the remaining 10 of the 20 steps run as plain Trotter. `aqc_options` raises the MPS bond dimension to `max_bond=128` for the longer, more-entangled chain (matching the original), keeping the same L-BFGS-B optimizer capped at 100 iterations. The `estimator_options` turn on the built-in error mitigation: dynamical decoupling (XY4), gate twirling, and TREX measurement mitigation. The function's defaults already match the original tutorial for all of these except the TREX learning budget (`measure_noise_learning`), which is the only genuine difference. The whole block is still written out because a caller-supplied `estimator_options` replaces the function's defaults wholesale instead of merging into them, so omitting a key would fall back to the Qiskit Runtime default rather than the function's."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "large-s3-run",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "job id (save this to reconnect later): 43ed8d07-6d7d-4f33-b70a-7f31b765b310\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Steps 2 + 3: the function compresses (varied ansatz) and executes on hardware.\n",
+    "job = fn.run(\n",
+    "    t_steps=time_steps,\n",
+    "    aqc_segments=[\n",
+    "        {\"n_steps\": 6, \"ansatz_steps\": 1},  # early steps -> shallow 1-layer ansatz\n",
+    "        {\"n_steps\": 4, \"ansatz_steps\": 2},  # later steps -> deeper 2-layer ansatz\n",
+    "    ],\n",
+    "    aqc_options={\n",
+    "        \"max_bond\": mps_max_bond,  # 128 for the longer chain\n",
+    "        \"cutoff\": mps_cutoff,\n",
+    "        \"optimizer_settings\": {\n",
+    "            \"method\": \"L-BFGS-B\",\n",
+    "            \"jac\": True,\n",
+    "            \"options\": {\"maxiter\": 100},\n",
+    "        },\n",
+    "    },\n",
+    "    dt=dt,\n",
+    "    hamiltonian=H,\n",
+    "    initial_state=gs_circuit,\n",
+    "    backend_name=\"ibm_pittsburgh\",\n",
+    "    # Mitigation settings from the original tutorial. Only the two\n",
+    "    # measure_noise_learning values differ from the function's defaults; the rest\n",
+    "    # restates them, because a caller-supplied estimator_options dict replaces the\n",
+    "    # function's defaults wholesale rather than merging into them.\n",
+    "    estimator_options={\n",
+    "        \"environment\": {\"job_tags\": [\"TUT-SNS\"]},\n",
+    "        \"dynamical_decoupling\": {\"enable\": True, \"sequence_type\": \"XY4\"},\n",
+    "        \"twirling\": {\n",
+    "            \"enable_gates\": True,\n",
+    "            \"num_randomizations\": 1000,\n",
+    "            \"shots_per_randomization\": 128,\n",
+    "        },\n",
+    "        \"resilience\": {\n",
+    "            \"measure_mitigation\": True,\n",
+    "            \"measure_noise_learning\": {\n",
+    "                \"num_randomizations\": 32,\n",
+    "                \"shots_per_randomization\": 100,\n",
+    "            },\n",
+    "        },\n",
+    "    },\n",
+    ")\n",
+    "print(\"job id (save this to reconnect later):\", job.job_id)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "large-reconnect-md",
+   "metadata": {},
+   "source": [
+    "<Admonition type=\"note\" title=\"Reconnecting to a long-running job\">\n",
+    "\n",
+    "The large-scale run is not quick, and most of the time is classical rather than on the QPU. The AQC compression runs inside the function before anything reaches the QPU: at 30 sites with `max_bond=128` that took close to four hours in our run, against the roughly 18 minutes of QPU time quoted in the *Usage estimate* above. Queue wait is on top of both. You do not need to keep this notebook or kernel open while it runs.\n",
+    "\n",
+    "Copy the job id printed above and save it. The next three cells let you pick the run back up later:\n",
+    "\n",
+    "1. Reconnect, only needed in a new kernel session: re-run the [Setup](#setup) cells to recreate `serverless`, then rebuild the `job` handle from the id you saved. Skip this cell if you are still in the session where you submitted, because the handle is already live.\n",
+    "2. Check status: re-run until it reports `DONE`.\n",
+    "3. Fetch the result: run only once the status is `DONE`.\n",
+    "\n",
+    "The reconnect cell below carries the job id from our own run. Paste yours over there:\n",
+    "\n",
+    "</Admonition>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "large-reconnect",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Reconnect to a previously submitted job by its id. Only needed in a NEW kernel\n",
+    "# session; if you are still in the session where you submitted, the `job` handle\n",
+    "# above is already live, so skip this cell. Replace the id below with your own.\n",
+    "job = serverless.get_job_by_id(\"<your job id>\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "large-status",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DONE\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check where the job is. Re-run this until it reports DONE before fetching the\n",
+    "# result below: OPTIMIZING_FOR_HARDWARE -> WAITING_FOR_QPU -> EXECUTING_QPU ->\n",
+    "# POST_PROCESSING -> DONE.\n",
+    "print(job.status())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "large-s3-result",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AQC fidelities: {'1': 1.0, '2': 0.9994, '3': 0.9944, '4': 0.9853, '5': 0.9747, '6': 0.959, '7': 0.9495, '8': 0.9542, '9': 0.9533, '10': 0.9451}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Run this only once the status cell above reports DONE. result() blocks until\n",
+    "# the job finishes, so calling it earlier just waits (possibly for hours).\n",
+    "result = job.result()\n",
+    "print(\n",
+    "    \"AQC fidelities:\",\n",
+    "    {k: round(v, 4) for k, v in result[\"metadata\"][\"aqc_fidelities\"].items()},\n",
+    ")\n",
+    "\n",
+    "ev = np.array(result[\"expectation_values\"])\n",
+    "Gjjc = ev[1:]  # drop the t = 0 row -> shape (time_steps, n)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "large-s4-md",
+   "metadata": {},
+   "source": [
+    "### Step 4: Post-process and return result in desired classical format\n",
+    "\n",
+    "Identical post-processing to the simulator run: Fourier-transform the Green's function into $S(q, \\omega)$, mirror-symmetrize, and clip negatives. With the longer chain and evolution the two-spinon continuum is far better resolved. It should fill the band between the dashed bounds, brightest near $q = \\pi$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "large-result",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 800x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "n = job.result()[\"metadata\"][\"n\"]\n",
+    "q_res, w_res = 100, 100\n",
+    "spectrum = get_spectrum(n, Gjjc, dt, time_steps, q_res, w_res)\n",
+    "spectrum = -(spectrum + spectrum[:, ::-1]) / 2  # mirror symmetry\n",
+    "spectrum = np.clip(spectrum, a_min=0, a_max=None)  # clip negatives\n",
+    "\n",
+    "plot_green(\n",
+    "    n,\n",
+    "    Gjjc,\n",
+    "    time_steps,\n",
+    "    dt,\n",
+    "    title=f\"Retarded Green's function - {n} qubits (AQC, hardware)\",\n",
+    ")\n",
+    "plot_spectrum(\n",
+    "    spectrum,\n",
+    "    dt,\n",
+    "    q_res,\n",
+    "    w_res,\n",
+    "    lower_bound=True,\n",
+    "    upper_bound=True,\n",
+    "    title=f\"Dynamical structure factor - {n} qubits (AQC, hardware)\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "nextsteps",
+   "metadata": {},
+   "source": [
+    "## Next steps\n",
+    "\n",
+    "<Admonition type=\"tip\" title=\"Recommendations\">\n",
+    "\n",
+    "- Adapt this workflow to your own system: the function is Hamiltonian-agnostic, so a different `SparsePauliOp`, initial state, or set of observables runs the same PRE &rarr; FUNCTION &rarr; POST pipeline. See the full input/output contract in the [AQC Dynamics Template](https://github.com/qiskit-community/qiskit-function-templates/tree/main/physics/aqc_trotter).\n",
+    "- Read the paper this benchmark comes from: Lee et al., [*Benchmarking quantum simulation with neutron-scattering experiments*](https://arxiv.org/abs/2603.15608) (arXiv:2603.15608).\n",
+    "- Compare with the [original \"Simulate neutron scattering\" tutorial](/docs/tutorials/simulate-neutron-scattering), the inline workflow this one ports onto a Qiskit Function.\n",
+    "- Go deeper on the [error mitigation and suppression techniques](/docs/guides/error-mitigation-and-suppression-techniques) applied on the hardware run: dynamical decoupling, Pauli twirling, and TREX.\n",
+    "\n",
+    "</Admonition>"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 08088ce280db4eed998840185f2262648f3aee86 Mon Sep 17 00:00:00 2001
From: Parth Danve <parth.danve@uconn.edu>
Date: Tue, 11 Aug 2026 16:37:59 -0400
Subject: [PATCH 02/25] CI, Workflow etc

---
 docs/guides/_toc.json                         |   4 ++
 .../function-template-aqc-trotter.ipynb       |  30 +++++++--
 docs/tutorials/_toc.json                      |   4 ++
 docs/tutorials/index.mdx                      |   2 +
 ...on-scattering-with-a-qiskit-function.ipynb |  62 +++++++++++++-----
 .../extracted-outputs/large-result-0.avif     | Bin 0 -> 14938 bytes
 .../extracted-outputs/large-result-1.avif     | Bin 0 -> 24495 bytes
 .../extracted-outputs/small-s4-code-0.avif    | Bin 0 -> 10564 bytes
 .../extracted-outputs/small-s4-code-1.avif    | Bin 0 -> 21467 bytes
 qiskit_bot.yaml                               |   4 ++
 scripts/config/notebook-testing.toml          |   5 ++
 11 files changed, 86 insertions(+), 25 deletions(-)
 create mode 100644 public/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/large-result-0.avif
 create mode 100644 public/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/large-result-1.avif
 create mode 100644 public/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/small-s4-code-0.avif
 create mode 100644 public/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/small-s4-code-1.avif

diff --git a/docs/guides/_toc.json b/docs/guides/_toc.json
index c544199ef51..40392520d78 100644
--- a/docs/guides/_toc.json
+++ b/docs/guides/_toc.json
@@ -825,6 +825,10 @@
             {
               "title": "Template for Hamiltonian simulation",
               "url": "/docs/guides/function-template-hamiltonian-simulation"
+            },
+            {
+              "title": "Template for AQC + Trotter Hamiltonian dynamics",
+              "url": "/docs/guides/function-template-aqc-trotter"
             }
           ]
         },
diff --git a/docs/guides/function-template-aqc-trotter.ipynb b/docs/guides/function-template-aqc-trotter.ipynb
index 8e9e3c77d83..3f73a51b881 100644
--- a/docs/guides/function-template-aqc-trotter.ipynb
+++ b/docs/guides/function-template-aqc-trotter.ipynb
@@ -15,6 +15,16 @@
     "# Deploy and run the AQC-Trotter-dynamics Qiskit Function"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "version-info",
+   "metadata": {
+    "tags": [
+     "version-info"
+    ]
+   },
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "id": "overview",
@@ -33,7 +43,7 @@
     "\n",
     "The template is published in the Qiskit function templates repository, alongside the other application templates. See: [AQC Dynamics Template](https://github.com/qiskit-community/qiskit-function-templates/tree/main/physics/aqc_trotter). This notebook deploys it to your own IBM Quantum&reg; Serverless account. Run it once, and any notebook can then call the function with `serverless.load(\"aqc-dynamics-function\")`.\n",
     "\n",
-    "For a worked scientific example, see [Simulate neutron scattering with an AQC-dynamics Qiskit Function](neutron-scattering-with-qiskit-functions.ipynb), which calls this function to compute the dynamical structure factor of KCuF$_3$. This notebook covers deployment and the input contract instead."
+    "For a worked scientific example, see [Simulate neutron scattering with an AQC-dynamics Qiskit Function](/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function), which calls this function to compute the dynamical structure factor of KCuF$_3$. This notebook covers deployment and the input contract instead."
    ]
   },
   {
@@ -352,8 +362,14 @@
     "job = fn.run(\n",
     "    t_steps=8,\n",
     "    aqc_segments=[\n",
-    "        {\"n_steps\": 4, \"ansatz_steps\": 1},  # early steps -> shallow 1-layer ansatz\n",
-    "        {\"n_steps\": 2, \"ansatz_steps\": 2},  # later steps -> deeper 2-layer ansatz\n",
+    "        {\n",
+    "            \"n_steps\": 4,\n",
+    "            \"ansatz_steps\": 1,\n",
+    "        },  # early steps -> shallow 1-layer ansatz\n",
+    "        {\n",
+    "            \"n_steps\": 2,\n",
+    "            \"ansatz_steps\": 2,\n",
+    "        },  # later steps -> deeper 2-layer ansatz\n",
     "    ],\n",
     "    hamiltonian=H,\n",
     "    aqc_options={\"max_bond\": 32},\n",
@@ -541,10 +557,10 @@
     "\n",
     "<Admonition type=\"tip\" title=\"Recommendations\">\n",
     "\n",
-    "- Work through [Simulate neutron scattering with an AQC-dynamics Qiskit Function](neutron-scattering-with-qiskit-functions.ipynb), the companion example that calls this deployed function to compute the dynamical structure factor of KCuF$_3$.\n",
+    "- Work through [Simulate neutron scattering with an AQC-dynamics Qiskit Function](/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function), the companion example that calls this deployed function to compute the dynamical structure factor of KCuF$_3$.\n",
     "- Read the [AQC Dynamics Function Template Github](https://github.com/qiskit-community/qiskit-function-templates/blob/main/physics/aqc_trotter/) for the complete input and output contract, further examples, and citation details.\n",
     "- Browse the [Qiskit function templates repository](https://github.com/qiskit-community/qiskit-function-templates/tree/main/physics/aqc_trotter) for other application templates built the same way.\n",
-    "- Read the [Qiskit Serverless guide](https://quantum.cloud.ibm.com/docs/en/guides/serverless) for managing deployed functions.\n",
+    "- Read the [Qiskit Serverless guide](/docs/guides/serverless) for managing deployed functions.\n",
     "- Go deeper on the AQC compression stage with the [Qiskit addon: AQC-Tensor](https://qiskit.github.io/qiskit-addon-aqc-tensor/) documentation.\n",
     "\n",
     "</Admonition>"
@@ -553,7 +569,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "neutron-scattering",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -567,7 +583,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.13"
+   "version": "3"
   }
  },
  "nbformat": 4,
diff --git a/docs/tutorials/_toc.json b/docs/tutorials/_toc.json
index 4e7370c233d..3787f0a0638 100644
--- a/docs/tutorials/_toc.json
+++ b/docs/tutorials/_toc.json
@@ -61,6 +61,10 @@
               "title": "Simulate neutron scattering in quantum materials with quantum circuits",
               "url": "/docs/tutorials/simulate-neutron-scattering"
             },
+            {
+              "title": "Simulate neutron scattering with an AQC-dynamics Qiskit Function",
+              "url": "/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function"
+            },
             {
               "title": "Krylov quantum diagonalization of lattice Hamiltonians",
               "url": "/docs/tutorials/krylov-quantum-diagonalization"
diff --git a/docs/tutorials/index.mdx b/docs/tutorials/index.mdx
index b3792b5ddbf..636e7bd2968 100644
--- a/docs/tutorials/index.mdx
+++ b/docs/tutorials/index.mdx
@@ -51,6 +51,8 @@ These tutorials focus on estimating physically meaningful quantities, such as en
 
 * [Simulate neutron scattering in quantum materials with quantum circuits](/docs/tutorials/simulate-neutron-scattering)
 
+* [Simulate neutron scattering with an AQC-dynamics Qiskit Function](/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function)
+
 * [Krylov quantum diagonalization of lattice Hamiltonians](/docs/tutorials/krylov-quantum-diagonalization)
 
 * [Nishimori phase transition](/docs/tutorials/nishimori-phase-transition)
diff --git a/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function.ipynb b/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function.ipynb
index f38200c2914..5430b8f60b0 100644
--- a/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function.ipynb
+++ b/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function.ipynb
@@ -10,7 +10,7 @@
     "description: Compute the dynamical structure factor S(q, w) of the quantum magnet KCuF3 by running an AQC-compressed Trotter workflow as a Qiskit Function.\n",
     "---\n",
     "\n",
-    "{/* cspell:ignore Trotter Trotterization spinon spinons KCuF quimb DMRG magnon antiferromagnetic Suzuki fidelities isa COBYQA */}\n",
+    "{/* cspell:ignore Trotter Trotterization spinon spinons KCuF quimb DMRG magnon antiferromagnetic Suzuki fidelities isa COBYQA Gjjc qpoints viridis fontsize vmax vmin */}\n",
     "\n",
     "# Simulate neutron scattering with an AQC-dynamics Qiskit Function\n",
     "*Usage estimate: 18 minutes on a Heron r3 processor (NOTE: This is an estimate only. Your runtime might vary.)*"
@@ -171,7 +171,13 @@
     "\n",
     "\n",
     "def plot_spectrum(\n",
-    "    dsf, dt, q_steps, w_steps, lower_bound=False, upper_bound=False, title=None\n",
+    "    dsf,\n",
+    "    dt,\n",
+    "    q_steps,\n",
+    "    w_steps,\n",
+    "    lower_bound=False,\n",
+    "    upper_bound=False,\n",
+    "    title=None,\n",
     "):\n",
     "    \"\"\"Heat-map of the dynamical structure factor.\"\"\"\n",
     "    omega_max = np.pi / dt\n",
@@ -220,7 +226,13 @@
     "    site_axis = np.arange(n)\n",
     "    x, y = np.meshgrid(t_axis, site_axis)\n",
     "    c = ax.pcolormesh(\n",
-    "        x, y, np.real(Gjjc).T, cmap=\"RdBu\", vmax=0.5, vmin=-0.5, shading=\"auto\"\n",
+    "        x,\n",
+    "        y,\n",
+    "        np.real(Gjjc).T,\n",
+    "        cmap=\"RdBu\",\n",
+    "        vmax=0.5,\n",
+    "        vmin=-0.5,\n",
+    "        shading=\"auto\",\n",
     "    )\n",
     "    fig.colorbar(c, ax=ax, label=r\"Re $G^R(j, j_c, t)$\")\n",
     "    ax.set_xlabel(r\"Time  ($t / J^{-1}$)\", fontsize=16)\n",
@@ -406,8 +418,12 @@
     "gs_circuit = prepare_ground_state(\n",
     "    n, gs_layers=3, max_bond=mps_max_bond, cutoff=mps_cutoff\n",
     ")\n",
-    "gs_circuit.rz(np.pi / 2, center)  # exp(-i (pi/2)/2 Z_center): the neutron perturbation\n",
-    "print(f\"Prepared {n}-qubit ground state with the neutron kick at site {center}.\")"
+    "gs_circuit.rz(\n",
+    "    np.pi / 2, center\n",
+    ")  # exp(-i (pi/2)/2 Z_center): the neutron perturbation\n",
+    "print(\n",
+    "    f\"Prepared {n}-qubit ground state with the neutron kick at site {center}.\"\n",
+    ")"
    ]
   },
   {
@@ -432,8 +448,14 @@
     "job = fn.run(\n",
     "    t_steps=time_steps,\n",
     "    aqc_segments=[\n",
-    "        {\"n_steps\": 3, \"ansatz_steps\": 1},  # early steps -> shallow 1-layer ansatz\n",
-    "        {\"n_steps\": 2, \"ansatz_steps\": 2},  # later steps -> deeper 2-layer ansatz\n",
+    "        {\n",
+    "            \"n_steps\": 3,\n",
+    "            \"ansatz_steps\": 1,\n",
+    "        },  # early steps -> shallow 1-layer ansatz\n",
+    "        {\n",
+    "            \"n_steps\": 2,\n",
+    "            \"ansatz_steps\": 2,\n",
+    "        },  # later steps -> deeper 2-layer ansatz\n",
     "    ],\n",
     "    aqc_options={\n",
     "        \"max_bond\": mps_max_bond,  # MPS bond dimension for AQC compression\n",
@@ -518,9 +540,8 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
       "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
+       "<Image src=\"/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/small-s4-code-0.avif\" alt=\"Output of the previous code cell\" />"
       ]
      },
      "metadata": {},
@@ -528,9 +549,8 @@
     },
     {
      "data": {
-      "image/png": "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",
       "text/plain": [
-       "<Figure size 800x500 with 2 Axes>"
+       "<Image src=\"/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/small-s4-code-1.avif\" alt=\"Output of the previous code cell\" />"
       ]
      },
      "metadata": {},
@@ -628,7 +648,9 @@
     "    n, gs_layers=5, max_bond=mps_max_bond, cutoff=mps_cutoff\n",
     ")\n",
     "gs_circuit.rz(np.pi / 2, center)  # neutron kick at the center site\n",
-    "print(f\"Prepared {n}-qubit ground state with the neutron kick at site {center}.\")"
+    "print(\n",
+    "    f\"Prepared {n}-qubit ground state with the neutron kick at site {center}.\"\n",
+    ")"
    ]
   },
   {
@@ -660,8 +682,14 @@
     "job = fn.run(\n",
     "    t_steps=time_steps,\n",
     "    aqc_segments=[\n",
-    "        {\"n_steps\": 6, \"ansatz_steps\": 1},  # early steps -> shallow 1-layer ansatz\n",
-    "        {\"n_steps\": 4, \"ansatz_steps\": 2},  # later steps -> deeper 2-layer ansatz\n",
+    "        {\n",
+    "            \"n_steps\": 6,\n",
+    "            \"ansatz_steps\": 1,\n",
+    "        },  # early steps -> shallow 1-layer ansatz\n",
+    "        {\n",
+    "            \"n_steps\": 4,\n",
+    "            \"ansatz_steps\": 2,\n",
+    "        },  # later steps -> deeper 2-layer ansatz\n",
     "    ],\n",
     "    aqc_options={\n",
     "        \"max_bond\": mps_max_bond,  # 128 for the longer chain\n",
@@ -799,9 +827,8 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
       "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
+       "<Image src=\"/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/large-result-0.avif\" alt=\"Output of the previous code cell\" />"
       ]
      },
      "metadata": {},
@@ -809,9 +836,8 @@
     },
     {
      "data": {
-      "image/png": "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",
       "text/plain": [
-       "<Figure size 800x500 with 2 Axes>"
+       "<Image src=\"/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/large-result-1.avif\" alt=\"Output of the previous code cell\" />"
       ]
      },
      "metadata": {},
diff --git a/public/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/large-result-0.avif b/public/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/large-result-0.avif
new file mode 100644
index 0000000000000000000000000000000000000000..ab48d3cd3b1524df907f35432ed8d53e73e8dae3
GIT binary patch
literal 14938
zcmYMbQ;;aY&NVu=ZQHi@*tTuk+GE?cZQHhO+w<>pzI*ECp|i49lCI8ES64Lv0054u
zvxmKbtA#1RKeMqgW&97eu`u`-1KQh|I2-(j|7V2e#@3Gi2LS-=EsUK1hyUkUEUfK}
z{u2lb00031{s#dj{htQ_;0XA~{WC7q|1kg}|3VH63)}y0{9hIIUtsdzw13qGt_+O-
zsrdiNf6D(zIC~3coByc)?N)1H?`Z!IM_AY!+5Ph{2Cj^M0zhE?qoY_j*_-?a0000(
z0ssKX{sRd17VZ}RWgs9R{`vneALl>Rzs~=s0scqfFmQ4I^B>y9(U!}`-o)&`ix8KQ
zg^@j%lZTVDiH#H2zZ?r|3j;?FE(2?O^Z(8Z*TUZBe}nrs3*Z1CU;rSHU?9+tf4#6Q
z98Lc3(EqmkS8V;?as4|o$Bd1!fin^S6ci?zeK>0Y5Ed*1)1AFEkGs36;_P&(G};8I
z+>>L2Ly55gryUjnV8Yeb$+oI}GpNzJhi5I-m`o5P#z8cX8J_D;QLWobH9ILx1%=&r
zkL{|I1%74sw};)g7a0}g9bxYipF^QVSH&wtN&I#(K({pTk1M4T0rgO)KtResw|M`I
zWj4<)a?FxW<^@3RxM%1>!*}+ba5;>#cb+{PvL2+`eJj595DXJDo`GgWnI`fu36KY;
zQ+cY?KxqYf20KRKDe&(t6HWgHcGfA?pX1uiufTf9Z1gP9AG=Y2eYZg;p8lFs{(DY6
zj->VRsLIuS&|@?CKgJhxArHkT!;{PPKl(%Ms*x}$Q4chLN#!FIeSO<cx}}b2Q*@M$
z=bH5G_>ieV<UKCmJF^X^5@$&KxU5Modr~=qSf`;Xzu|AvrFwg@y+_uEu}bSdhaYt-
z-kG(YcWN*G{o(1HPJEZCFtO>4U@0yeUm(>*)lBA-RLK_cAplChR0U9g?{AIB@|}wn
z-)g;IyCS1lXnva#E)E(ZMRas@_j=;IDsVJ$@4w*(-}qR!SiBlbXL0oDCDTK--LVLM
zFi3z&$e;}i+>E<67)b<iVS%pz2OF5ebBCV5URpequIwMYS9S`rcX1b&r4Em$o>CoP
zBRdG4jn-lVpC^5OT0^b6t}!xh)MRnIT~Ry3EloKE)|MS;XC%%AdHd?nRU7r^=vqbS
zdh0h*Um1K=H_2{z!N^sBisrZhR<DuF`CwSw)EA{E=a$Y1>q05uAfc9@CRSC-Ac9*=
zv~^zT;}Pg%sq@kH>IOy5JhJu((Bzl%`fKQJI=dOM`~`Mgw1k4AvXx0<BXQdMKUUb)
z^;wOWD)!97ZcqL4kh(Am_RBWAKdtmC98ZVqZI%S;TG;p?9Tm2uX`Fatnp_}P^;Cgp
zY`Wy+RG3vY+bdQfb4UWX*t)VMG(A^bZk;{oVO2Yvo9MzD(TzI#ED^_nbDESA*D14y
zl|{a@*VE{6xn^F{oIiJVf)f6I-d%lwJJBIT(QCgeh_boywKtg8A4mDF@Di$u<&(XY
z3JBXNDpNBNe8){q&s+%Iu-jO04^ktX_f00f?<f6?OSJrsd6B7L-9>NW$A8rrJiq%J
zWtHo+1R?goK-!?Gt-MhBQP?;rhmj@{)gA};6y}PvW)eJyIP!VOV#uGoJW*D{bV^QN
z31EHsb*ZVN;?tVal<c9Xyuhu{qJqByW`KRp%AKpi1dgOy<l;QAZMEj_4biy&45q;=
z?Ox!~;I?K|xJYRF_bw@LYjcap!eu7yL}2^8I*mU16>r6JB$TS6K-cs|>hhMNKeF%#
zs&T_{fG(|gIM!gDgLcSopi)MV;yskQ;0#h$+?qZYC^F<)TkOFC3rgtbe7McpJ!m1g
z=K9bhIBB9}?tG;n5YHnDOW<`sP4Ns`k~NywW}?!zJea@B8vkkZ(2i)83X|;;$KN5;
z5nowO_M?By-7nEt?h!10n&*l=vn={4ZzM~duh>7Dqi47mf**_LD>H(#-rj?FI^%kE
zhnVwa*3v)c&}sQh0w1y+(esU`Ar6A{U{p(#y=U++nfV1lq)JBBly3}8=3Hg_$^k+H
z1{7lAX){^sLkuH0RkrDE)SDD)Cz5`JlL3CrVV4TBes1#RdaCZ8A`~*SNd`GVQwsvm
zDLN_!3GIp{@2+X$Q|f0dx6kxl_XEe*X_rw=yBwA_VJad%Fv^HD6g@v0D`QwG<UFZz
z-`|Tq_VDUrMvytylSzteB;8yR#Vv%)>I0xKBU1mqrw9txL%AonhE=W!=sScr#3WCM
zwJGX;cJ*HlW~Dq5(Bl~h89zLzzzGv)<`nDWuDr=gs;{+ess^3BTBphNy6-}@iyQNk
zWMSlnEY{ac_f*^RX@wEmOJDf8d(jG|gmpxxQ!p;;&s5^L6D(EG^lNcRR`}gWxpc-|
zd5OPUWRvsbRW!cfjgiBNhshw2?XbcjF|OJ>ofO~T9uTIgA;UTFv$qmlymouZ?_GS9
zv<knV9Ihj-$T-zVWv_2%t%>YQEExO)lgi`bjg-{Xf_}Ks7q;J&baFl=`)U?0YNho3
zi`<j=?iKXyisEa;g8g+yhR^`O-<}9BN5)+s@3lCsb8G)g(Ns)B5?i$ZKf1qY%!x4E
zhpyAp`2J%#Tn1WX;6MlG@wqPEslw~)1AU=%TVJtE^s&;vwrrh)K8dvM$K?Y#F10o6
zZDs4a$c$y?9C75CA~k{sP^JT(Z_}}@ZbTr7Ys>z2EIJlS$FmVcBETr!d>Gj$SH=Ey
zoZz&jG9laI`jkizIeuQVWd?LSeQA2%Aqklddh<QEl57y^2qSdx${~X_H*$_8g3g{y
z5uJa0HtW^2CmLG9UlxY7i=FJnj#JNbE697Qe$%W&a~$076PJ`e2Ys+faRYe?c9>zS
zbi=u;e`_g!fJVo5JZLX?#qSsi2ZKMsFw#hg5SH4DDq3`=N(NISAWfQk$JFy#jE?Fc
zxBW=7Fi*jELO9aOt-t9KaVt{Yk4Z&qq|4mDuwM-r-J!S4<&Js#tvR3yy68D02lD{x
z!FKd6Kh@ruRhBMcx>UNBp|VFv_@hCSv}@}upn@#aEy50l=p)B!37-Qx)bU-CfsWNV
z4T%Bwa)pj|LHm#xCtU;uEV`(#4~#mMiShtf;upT0oC$wN4U6sC<dPVYp_GhIeQoJQ
z%*Q-1*e)C6Y2kjJ-@z0dWZ773MujQD05-GX?Tczdn|XhPNDX<=YRuUCsa5IWxJI}h
zz{?RQl{oSt@f5-fA!JkK0)I3q&kE~Y9t_*^TWH&?a9^3|+Xp;TyuNabjlk|Fe=EuQ
zxFl<;FaoZ{ROY4aRGQ9tS2AxKfGWgETD5EYrG);+Lpd+hA%wnw#Ze9<v4~9cqu6a|
z9+x!fkE}Gahxcorki>?fNgbw|$1a@K4#yyX>}6eAbp8+7+omgoyA{R6-0<#;$Wrbv
zQI&35o1zXkUpsRdeiODdV!A%qK@W3E`>x}AE&SDthd%INe@BW2?4VoF@(Z`1oGr-q
z;MqDGCNy_Ptj=5i&d|XQ3m8fO?}+)wZn>H@^GvW9;s`X}FIh%zX)mG&U{I$I3fggo
zNr{lR5H?vq-QfxPkKaK3w0jD*K0)#lypAw-D8}H$@SK^!O2_&(NSPYgV=CCB+0=bq
zl!vV3TX}Ytv>)O|QI_X%)tEv<cZlqJSxFE=sv;xYeY{6~_XU^4##(Ye)a<>H;qV}b
zTw*)S^8!ANI_lP5@)Obg-bI6syA+kxK%y8MNJa(w8e))0J;wL+q}r^foc15?0Eyma
zj249UkS_ZD%oqR!QTn8H(G@%Y)DfIA2980B*|}X4byfO{Je)2JXPJm#8M_NIZ-Y%#
zch`-sUxm7@fbIccIwUlFk&-irVMt9IU#~~P^fD{Hia=)Ch2242aiJF8S?*fv3|AA_
zhL3S}fyNJUL^btM+GZ50GS34TO<nvbNvs##&pB#fZfR?Lj1=<*xypSYCqfDl!E`_F
zO)oO>Y;iR|7AhON@{Fm>_dkR7G=CU;%+~VEr(NShr~-fxr!T88_3H&?guKyy_%CLt
zfg5csl0IZCnqA48pk~^sI9lgC!x!Et5jjP&TFLx1)|rGP`6qk)jw5vmT)JsLS%{|u
zsR2RgF<MYd1R=ViPiElSydw3Z_=ma4#HlNyG!K}xp*7s>-ciC*$cc#gv%q4dv_LSH
zoLlVmOoUP<C}%nVuPV3;`ldx<q^ehrao;Hl5QeuixS2vhRWNnkiH>_j+YNx9Is&qr
z*aQF%)U7aGTIln-+cK>To#Grxw-b=b3r&Ka!)_AoAmZsL{e{SSGV>A9#84Po8gS@>
zQsR+e!Q`1kiFIGE>)wg#Zz6xBE&7TY)$mm^I3yDDZEKGX>2(A>aSS5V=O~02jg~z;
z?*}0cFB8czx60di7WuP=(eie$`f33nPI~y84|Vx@!$qW4*z|tg49!YXIYx6;V$6wZ
z_I??_N5I#i!+Pd=*M|;$e_rW_rDw=gm#U+^(SdaIRwc}P#KabONH~sFr$gf|r?j(%
zd##*cArf$RVTTF|VLWqoNbFY1j1g@qI?87LqK^ELFjtVvrR8)5;vJCKLwNmV@S0o9
z-7wfl(%=li2cjZ|Gv6E4P?b{Xp>7_^W1e%B0kH=MtqMoDMhD#G)BbQqqyl)Ov8=ti
zq*RizMKz;g|CC^?NTZ;<8O%ib4G8tG0JD2t%Z^mtJeq9d*<OALGvx7jGelJ9NGK3?
znFNd(OR>Z+Uq19)`y;PMmVx_u$-=#OkhtvnS;DpZ0vH4c@CNE@@a-gMa&yiPMVJO2
zrb|QC591gzXJD5Bxa{wCov$vgSs4MHCr>f=LoVB^^2McwUNbs==DmS)v<vtvqB<gh
zIH)SWq8MNzhA*&N4c`s*+o9;?os}oJXe67M$K_MR(dU5S$;wbRypZk2fmE$2z~Cp~
z41&{813+<g{ywpnr&qUc$AZ3p7)K<U{W&(+kzk-mpCXUZNPi&2zh}J}S-MD#Li^*^
zKKmS1Tx-M8j*NM1#}lepn8q6L==B!4?7;{D(K`W-NJQhgBo$6i+q)TJgwB%*!$gL(
z!#DV@&G)r*X?=3RB%Ev(6i~;=G<b{Oy$fQ^6N{rV<!Xo!6HI+%MrC5ZtHugC=5N-C
z8r+gVR7)V>ge>lb*!cl6Hy9y_J^1v1fTGBS<OscA9+PtKEdThs4r(}61@W`R_820S
zImz3v#AyfjCE#kzAWSyVs25>01x*m0CJS{_KB1Z)cI*X4(B9ZDSpoDwM|hAYUT0%n
zaE(uS1M7$)0N6rQGO3HCo0dmD8`GXR3bWSE7cpI-*yv$9`W<^NbM#gnN%QRcm85f_
z^iG6%jr90jqLIrT=h#X<^KeT!heQ0{v}-VS`u%&iTehlS4OE^8NL%bGvk1*w+FWx7
zlZtD+I(`i2emLzTp|{whcd~jU+H=JXt|qa2ie}#gL<R&L11e1K#}XARtP(S<)_jD{
z-ZlE345th$Kz(9vnvTdN$z)Di@ag#&K*YiZEvf>7DoPQN#cMSmyaEgdXqN~dNW2c$
z%5EQ&%qY)^hnx9HD`BVrk+Yv6R8}De4S<0_rs@ZZV5?7!g}g-w56WerIv5udqD+@w
z(TsJ~hco=QYwK#`=yADNBj?z{K!rWYE^*UCgBzt1qKh=@uMhQLan3N<Pl2ahNTho2
zumM?&m&4Z&9;9NxA7g~BgdUZylgJ*1@7*2BF+b(p=~3MMHKUK-toe~mlm5msG%8d!
zO*Vpz?7lymNPT06P59G~>mV4Y>xIlhCp-3MJ=TA(QNV{xuCS5<q|ZN7V=4Y3(JzS>
zj`q*4I=1iq%1fRYo>W}ORl{ctZLu`u)U;)v^vec`O*J9VAK#$Zguwd}eKM(1oA4VB
zKF}spONki#isQJDz3kY>C(E0pl}7=O-bJ3wrjiift^-016*5I3kaSlE`K)gda;tzJ
zBWBL%!?C4x*v|1i?ts<~zVI48&rz11w2Fnt5=k{E;@})H&zOJ5U-_6_mOp8QzRdPK
zH>y0j`v+&Qil1DV3+!$D-jOSZT%sNBx6O}XoYEnti^@UTVMoMuMz=XCZz{g}&PGuM
z>mo8?^>Yl25}0ohBw^_&QzTQznm)GM0z?1@JCo1;;#n-?hoTC5hEJ2NnvbZwd-YZV
zpQ-H=QFL{J=J*E7sMqY>8U$P_OEE=x5oE+3*eTE|@eRD$*?Hyt3Cq1aY+bSk+Tmu&
zkcy){$*@k)Oh%3Law==R!e%69%uUKp{QH@4kEo>Y-$%D4=dOcL5*z?RbrI!CsK{=t
z1Ph<U?M#8ru6~NgLl*$D<$xrWSk^gUF&F!Wkr_+El#m8Bv~}$>+oBFdy;Bd#`*`+|
z)IM2L-2+&dIJwJZn7SZj=S@88+}B!qPHW*%iAi%O99hS0P?2ygfC<7)ueP-AoQ((P
zx425asay&vH$rpUz`eu%Iuebd-*zNE-1bTJbfy~L2J_v$wpz~mlS)YFl!>RM)ZEAU
zfNigb2FB%MrfvOWwv$ERdu)RSL5-hNh2=Ks_OPI@s7_LDuo)Yiov%c9kQu+~79cXR
zHRF?dZuB#$T<6?Mu~8xb#V&TS`}7gI3b7>e-N){%Ht5*yw8gC}Je#7rSQ;wjooTky
zUFa0AvahxY3TRZB%X{@yzi0=+;W{z4M~NyzPo4&`#j?03S#$f%j)}Ak<!1Pey}@2R
z+wsAkMQs$#?i>__vH>{n2Zj6<T!P@eno-*VQbFnHj@k-hG}`o+l(t>ToQBK!&lP)q
zn*s5;dH<g~FOR%8^}p0{@ZjHYkxG5q3vVwcrxk;ZTvIGRw{IV{&olK<81iXOX`2O?
zkwl$XtZ%;;blS2eWxYtvOLKujSOAu6FcD4ruK1t_t1SaWVj7!6G#xVnrDEJ-q#Hq0
zM6@>SD5%Q|r3@-P8Sr+Z;*iUFQj2i>$W7?t&4oj|dF}x7em??N%}^JYecIv2S|(#+
zf5m~!6N$z|c<Pp7c3o!e>n;$G+0_73Rs`CJb(w>p6#4(61vb!PJuegGoTh}<L0X3E
zPv<>e+Zi5-XYoMLC_+uTZn2nje$3}nTvuz!f!<QDmKKszx48UDl!09CJ3cS4NxZLP
zdZMD?UP^Q9vQnE;(wOZ7u;dQ^yB^dFD|FwjNb3MYIY@X1kp}y`s<}(@z%y3obRftG
z?DkBt>HJwC*eye)f3Fuy4!@%%MCZ2wD`CnM>_&MU>=Xnuf;84x1Vth^6lH;g#i5Iy
zWKh7TD<6)1eRi7mj{hyY2Qq%*a44HNI#bIhj3Y&*IlwDHFb2aaOGaNnXYOxD8>PN|
z{~HLVNg@-7%$A`52%_p=R0b{Qdq5a2&MJA<v;Q7vZCw9Qd@X_KfMj&Ad6Ngap9d%h
zK8KOY!B@LI1$bl{Lj35KV_^D&$9FyaeuaQdbvoa`GP)_SMaMAL9418{9+y1S4L=V~
zedzh_(rSp!D}+Hjqg9E2Q?`)*HGYAP1g^k5H21#j-A^|Uf^HG68@o?}1)M*b0xSdI
zJrVDZ8J+*(=Fxt*uUFmS%vv3^-#kigaqto%Kmt;qQz$(OvvfG(R=uLx>aF{BXva;_
zbo6X`CE8Wx7`ydeoIXx4MpqlBB`+0=(yiVCojAb3oj=gNM8wZ8bszZZT;_=vUMnH)
zy!e*;<k785iiO4BbE+Tj#RXgG_L#SiGtS+37U6)F3SX)5kWRcOEnjd^T;_p;#C+#B
zW@j+94iXf&3kRE4S4$ZaC<#I11DAY_>K@n@D)XijDkF;-k-#P2I-&z)$4*PDqd%U=
zaV>TILHl`JFW*c}MO9IJ4i3(<HA5WK;_FKXNjgmXY&x#=`-m^fS%jyo_?-;>_`_5A
z4l_rDfW%{H7sV4C56dctttSd(ND%bKjvy*e3qF<jC{5BdijG{jLXcARq&T+a{TBJ|
zKwpR6ojA5EO<#{hYZ&y}<_rW5U-=FstM9!|ZQ*XqqT*P`5dM{8fgmVi77{utUzaqL
z>o=35-YV>#*Q0Cy&Y?iN5j}y!I_mN7>5s4DmE~1pK4Q?vhN?qVP5bo|lnd)_w9&;%
zcL#s^NP4-CptH~R%@~Q_(7oiMrWW911e52Zp;_vOyiMrAvA*g~8_hph1?9DDq}(CC
zUM-deLA^rlfMNn#{0%89t4>;FC^)q5CI?9K@biN)2T1-bZn7|hRZ8?)HS7huMfc!c
zjN>mx!03*GS-%<8j@l(Xl5$Jl8K0is2e_WBE`|qQybvqViT4!KH>8&h$#$Yyd%%v%
zVizJYyzng$bVpq(YY+!*^ItcHFzfD#3BMr{*eV}!KSGFsES&k<q}%<5>?g}@`RUr9
z%5v;fk~>RTW8X7ghuVRDJ=<uePP7;8O)ihKNePdk`$@sqmrs!LrDQ)W4>rB|C8y%M
zoSzymP<DefmH|Bwt%k|jqULcBR1w5{u%dyy+R#uYU-sR3xQrv{!I`9#vU&^bhSo9~
zIL2j>oaC7HQEI==Pk^*q(v&6%=qM-Yxu#z7?m?SnmFks~m#rY^az-(4<D)u|eSHp#
zu}gj?S<9N>H?{}O*vvM`CR=F?8s=Rs?ighvjo95yPn8_i2QH%uP%O-Lul~i_rQpgZ
zMLpqu*10Vu7*6}k=l8;x@B$o8&g+L2IP&Qj3u};R0Dk*mqxo!?YCRw56@!lStcc(y
zYNskOPi(Y0HePoX?RF}UROr24HyNtzF><<ee^qM`V%HSI--V?^W-UjOavZZBxSoYw
zHT}hMGw|h(G4g`hJj0>MWN6qzIy~F_BRMvBnEqc|T^{z=w&NAmE)JRLc7Hsy<|R9!
z*ROkmrMvQ?y?t;!*N}Q;lon50W|j1}pnwwW2vhuyhn);#X@G`)gbyU99`lrei`|b^
z&o^#wlXMAe3frOb-gXYQr-#Fu0_F%Tua=Nr42a->Qu|BSF_<%}41^A3W#e`{AgcNW
z@0%k)*4LjCK0dJbCQAWO>gQg4-Q&b7NRM0j5|VT9EZ{Z}1&M-i&|hqf2KAQhL3Rcw
zA##l1s6=Q=cdh5$F74xfu%iZZ!A~l<e!AxH6(MSmdVwad!aNA!TG#P$lRNn;1sr6j
zu{*WJRrfxH?vT%E^rrLOvh=D!1R2`u!${dVAx;kBiCsa8cQTZ|G}CSNRNicFdVdcM
z7+OdbtVE6}3MgcGOIM{3G)97&$QZDdqQQ6bjWf_it4YeZh0tM`stUFz@|6fRf3al(
zXYFhL<b)@ya1<&dbgZQO=4^1UtCGB^t^AcxM{Gjh9Ai2MTMgk{>{Ga-%d;^y5YADH
zP8dlb`1yk8xp~*7!7A=QYM3$n;DWjL@YM3Lk<jQq9!koF&INbd{V}wVr(LLvMaB^<
z_X_jUZGd6An?!D5HAmno*hR8?Z48eu#U#>8LiPrqp0iAed1yEb*k$g2lYz&*+%eMX
zcuvSG!uEjyK0mY=tN14=*&AWK7~kv0Y9&sJ3nFMr`ylTaVwBO$m#Nm(kp0%=RKV(I
zp~Tiy_tfm#*(Fvr@cmOQY4GBDpl?q|u)j0lS<|<(B9O}YjM@=vk<y086(2AT)X;o$
zH!|r+1JqEYie>8b7Vc=7J_~NUSh&RIvOlZ?4b}$74O|dqTTb(&gBxruN=5X)QtC8h
zAJkK&-!5gGj1e!U1wIy;$OB>K76+(qJP<5_3F!fbt(uwaBVzp}S`ujktiFG$gKh?%
zD!R6D;Oa6>3A!sogPWObjJq1<%!Y7D=-Zc=%$$1AvIm;qS-s8rV!(0{YY^oZ{h9K5
ziGSPHqTi136EZ;JJ)IbbRGOB533Uqoj%No-#8RcA*^sh-#A`|eRX%{x8Rd=I26FcI
zHMbSHjWx`_%}%P|1Epqo&elvuO!;G)yxltlCqcMH9s4a0cq6kuf!vIX6>uK?1Te)$
zorly6R-$opW5*B^KMv(OCAZrYhy`lc>Dex6RZYEf@E@`R6i+H;-AW-9a#1r60{x_o
z&!Mp**J>N>Zt}!@#i9lco75Ym{mHL;z?`Ky+S@pHuE8g~M6C98DCXn}{2*M4CE<JV
zzMZMbsUlH%{8ODpk0>K|z2?<H&Fwmd@GQ<bTynSj7y+6#Qa&&|!Lu+C-rJQIc@4RS
zXaEt{cKFc*KMVg>%(yu8mPtuAq<Hk2(NG??b$E#br~A$bygZ{!#=r7v%6lixXpw){
zoASWXJwe@Oo|RuVugoiQB}|ls2lF~P;Q4|%KYI9Z4x%+;Ea1f=ccwazA-$JcO+vdG
zQY~q;wi(yNrJY)do}_-LmGi-etinKTnCm1mb30$Qxq*C!j8>o3P+V6x^D_HuLs}yK
zQtS)r**1JlcV*FD?-XAAg_htYy@H{I(BHVD12F{@Jp;w1qQDuw?%W)HN7Jwt8<(Hl
znAM2Fqb2FK^B$F1OB62_0HjMk7slUxOl?)>V{a1n4$ph(d6L*Hpv<+PpShNk@W4DL
zk~Cu)6&Tioj_^$eIP-Aw#F8LStdqpwT`P!f-ZQKShQj*!7A1hdO1v~5qtK8^<-sam
zLrWdoI&Xm1yw3qkaDjg>jDkg7Z-HA)zCu2kx7t3G+?Nudj9k=oU<_}~RJn~KUqvNl
zL~fL<MNeUJ&Gy0pruRp1$-)QCXCbkSs}~g97Zr4ABzut**26=9G;-~kyF-FWAhfdd
zfnyn3+in}OgY6Ng(;trY+t?%7F_P<LF~lTt0VGgK%fUuG>B9l`uol2hMkSc0k0_#f
zFL<39xdjnv%ru`clEr!jlYzF|6eh|p&yvj&HJdoLksMKR<F{N%p)9xwqsP6xy!6D%
z!cxcoL6VNW*m9c?3Yx3mZ(2q#lihTzKs)-UH<0obqV+tj(>Pbjb5$VRA}Ctc?*rwK
zW}}$v1$X4}c$6b=fan47QyJRvP4rDY?=F25<Dy+{Z-?6MiWu7UBi_r&#6coE7vBm>
z8YWSl7o>npkhln$+Iyg!+>CbT91d)1L#NK2H`cO?|C}uw!EtwE#M2r-hM0LHn!R}`
z1biR-s{aU75EHzGKdq5gvr7BxW$%v@(a8aY#%S&lFyVE|bpe@|OW)|4igL4FNtPpG
z@5vR3uoTcsurYd{m9^<9U8gGZtSqUWMM=7abGwk4T7Wd_BsqKIBY6n6JGX+|`J>7}
zN?zJGpzg#eS+1Nc!Rz|FCK{3Pe!9=nXa%GIh7$QK6BFW$Bta^vj_`8)cQ`?LfzOmS
zdaYCK1BHXqNXz&)G!gQtd8LjlmfZeajrUL>aPCu+fMn<^K~dZ{oLlYqEJOe;Zd=zl
z|IqTw@y5mHd-JmVLC&#uaf=p1L|QTqo3Wj}`Sf)?vR2U;*Cm=4&{mW!Bw+2>uUZ95
z*+;TK)k88)*r8RR&TvrAkQH&?npSHu`cZ2B?B4yD1gDofF99Rc!rr1g80VW;XzXn{
zj+=%tJa6XCJMQ=OQ_99E_!PW;mT2x9w)!T&)vNT%Xj-zO$nvc){^Yxbx8>fzLCm6w
z;!|W;(wVpA?N1%bq)``w!H07S%td9F{^o`$jg(8#hb;*jf~`1W@KrMtr5!ce-p|0%
zvg=kI9AyVImJiD^A!}MnUEYF|tR$It=WfyHofstCU+yX(bs8_LC1Li@1HlsubPI#>
zpC#|(?VXt3L@zp=XGqugL3F*zGD!WY3iNL?@t$TQ(mWTfUPS%n8WJ6hM}H00ozgt|
zs7|97x-xp>ol)%|MCf0R_n{VO_qrGISJ7<D+XGNjpYGs;+7e&PbLPH27No3YO(@(S
z)5u7B(xgL6%bHdId<5{Rbo}Qw3%hj8Jr@YIwJ~{6mdWVqeBj|U9hGQY>A}Cgk=kx-
z0o?lc)skHGkyMcYC}kZY@pC#AVA;!zLm}fG6HG~)c6ig8J$)$!bfPPM=Iq`-P0~ig
z6TkgipruHw$myf18do8*kAGq6%lm>Q#NBc~4jn*9_?N>Mw8j*LIB2~Y(d7}hV0X2c
zF-1o2HFQD>BNg^qch9BEq3S(qm!B`rN0=hh<e0D}vnJT{ZOsW)9_4PS*Z{i!Y{k;5
zu)X)u9VckaRNxfDj-^=r8D*LcR62YmxX6^F9EiAF0ERf?_-o?o24mgBw?t$>BSD(z
z`Thov-p#&;PhlHr!CJ>&t_TFQm+b3wr6MD*e!4o4t*$`hKB#<3n$*+fCT%399cV)o
zNATBegBw6E&jn4@+lPJEZmM$t%;Z#BDYhg}+|s*#?M;4>d6b2@x6272mC2<PWSYw^
zb17RZss0BaCE$=*zO?qL@?s23hAP}Ps=SSRE1zNOl+amdv&4fkN<Bc7O1Dx$L*}D^
zdl^xes3uRr0xc_#Hmi6U_c2kKCaK;mh5o#4Zpx!twKNo&36rAq)&JHS7j~29=vI}6
zwJp}5Ct+Cdg7oWc=85k)@WJ83#Yt1mJwU4A)!W#9V05S<&;73s_?&qwmcj_8rcg-4
zcE6C@RTGP(aJ(+DiZ6>hct4cdC_m!E8YfU-egyac-LQqsJ(R48*%>nE1q&Yk6EYnr
z3RxrBj0AkKLf*k{`V+jWGLR=;Nt)fjLv+|jl(;5*H5sJ&rYcR6_F2H{vS!1we&|F*
zaj}*bvRAy==jnzFKzEFEnvet!D~_Ttg|<vmkUH%0Ko;ciA1}p7{J!0P?*ZiSc49KP
zB43XjuuHAGitA+2bP>><sYaw|yIy6S49Y^!Ro{uH^O1Xi3sPfpJ&XnZa4J}RtH%De
zLJ{7esQt)!+xwq$AlFV7deUjhve303&UslxeaV;sJz<COeHgAOY{6No3m&Jwnj{@P
zw^t^FQ$sW-&TL-okfltA?Mot&W~%`RtCfF+WXzB_XIX#g6ELJ#=`b;E(XnZpRV7JS
zV@TK^;mU^jL9e)+a@;8SVYcTRW-vyML2EpPWuIRRdi_;X&7v<6BD(zL-oLSIUiSkT
z{oCrAUC__+6OP5MK^LyNGdl{|cDHzCq5-+x_6CTbhg{;#3HBdxUCA_&F|#iB|6s`4
z{eY-5u?OT1WgR%cE%Cq*S|4GmF_*&b_?fU~jVb-%YnRs93eoKcr{zYdj?lB@E+^Hm
zu`}x0;zp-}=^!v6dD#L>g}kAUf^9QVbjVjQHb*NAClT*zGm3N+k<5uN(|E=8C;nkg
zhJ*w%zSK_;p;h_ei@vE_BzrDIBZuoJJq`<!bOQ#7XPUSY-hMWJpDsqan2Se&c+3Dy
z4>wO*_-voAa|N0|7#FFRxsV2SWErp|+3J2MG;+N{LOhOLbdMo(JWZk$q1+CU-WF!J
zN;dmpk%HaM9@vB@et_7_T$pb}4ewiIg(N**wf<Uqqlt%R(7{Tpi9j;+NSha%Rr!hF
zOtwAT#V$P3Ud&D)OJ_-+fdI*I9`k5|O%fk@J{VsF@4xTqs_8_IxBJC41Kd(V&MO=U
z<M#O}iln8l^ae!P-*z;5#}txEN3^;yt>0Q~4K?%v54px0V5gzQqt>m0XT;a()F3+^
zd1Emq9dHHz6W-T>Cv|z4w?8zeeY0-XlfBFmf#WdnGdJv$c@SnUK|_2xA_HkLyGVZz
zucYdhQ32vb1Pl?i@exykb$F^IjE^sdtCU7u>ucD#1_&<@xmJU#nblMZRb8#Ogrz;g
zn*3V{Otzg*TNx{i7%?=wsW^G5SG0#;W`oguBZ1Vr1dLx~qh96LD~C$jm-VQ{bZ(?R
zVxd4XXaBd2r=TIO>}P{P4or>@rs;(j&L8iU;12h~QNg}?J@}z(k2Xk1ICL4HaZDb^
zB$UhcY;$SVH|<f_!%fa6Y&*{)I$e`~*-1|=%Xw%8@ART%3dl{Wn6cHP9wlD2xz~sl
z#`t=U-<4I=b^uy(a}rKMKgSF|_(P!LU2}A|xu=wiTfy+<ZeXvd4rWHX93ZO18{~~@
z&lcLtTJXs?X59tLc{{nO5?+CU-1k|YlT(#66l@z@HD-k+d)64}2rpsY!h@uQt*WX4
zl~`wtEtBv1SWtT^C4>l4*NGgi$6#AfxRkj~%c7{dE;@#r1Qz&-`vRU8zQS~|IJ>Vv
z-U0+KcPH8<zr5Dy4}%zKa6o%#sKT;?5Py&m({G}5{7%<*(wP~7(QvmM4g;m}_9e@V
zp!H}Yx;Yzj--|T+aCb(UiJHUtqHJ3t1WNPpOf}eti1u)_)jhDX(+A}Z6vnR~_pC1%
zb$916B*3aIXEl)z)^W-XB2v@32yU&P<Q%2+V$Rd?A=5hV77mbP){hLbj9UO=c-V!A
zrFeh_&^o*P`}is0m0?Gk*NL3?_X#%6h8SgxZeq@`ZO*Gu_gFcS+$zV6q%VJ!$>@`6
zfRkv>3pr!(W1((cEIT%CDvS4s67AgH2QyPkNbHs6C@DHg&+*%YVlz#BXH+cXgU7uT
zDSvqTCaIC_8Fy-__?v3ZEb3r2DK%N5o*s1=8*wb?w8!ZsLeFQl5H=7-z*1JM4*#NJ
z;MB)VAH|;wa9!C%BYruke(oL-;P`UYg#IK&_9vc4bDG{*lTeF#{)8$m)lk{+EFAXq
zC%oO(g2cH!U~+Ay?j>;icu4x1O@!XN`>QPDyp=&&VogFCzxnrovSCeZ+Kon4znPiy
zvggUlm`GO8&`GNv=*lH+fG$11uakNuAe*Y6`@DoIt1kzTRJ2<pTXwV=N28ba1kTaU
z<tgaBo~6%Q3#nla^P-`QcTF({;R7ZHy8OLrXx$xLWHP{lm6q;GN+QdSkzGz_2_+1L
zN{TaE!*bX&mvPwD7+Y371v#hcM=&7EfR#p((qu(|_F7F<VqJeVh!!Q|)e|kruG4N4
z9917?;`9|=LN|_b&Wm+V!;-#g5)m{I3Jk5)T`A;%t;>QSe&ZUcM&!WIAMlwpAixmM
z^>>XEk#lV0`558J447#^(CzLI4SQ)m(kr=C*!M#gmrBS0D1(DAnApUGC|pZPVRN_{
zdm-sIaV{PzjV8><7WFY@G?oF(sib%W=Jz~ok!coCCL_Jwm3n&GCPQ93B7a!81b;xk
zDc?gA>9U|vrM}v_<}72LfrWi0>gprl4ir8rEy!pqP|q!OtovV^YoDpQXCBDq1-s0I
z+4t4ZL(}eoSG?{|0@f>~$`EwjOf0$lt<ozFt|FJKaH7|0JVXOERnZ)4pMHDHF6Kbn
zjHF&N@G$3lsJ*I$t{l_xM1&vrA}7ujaOeRvO==V1ANBymPKX;Ymy~N(E?#lLmJMg6
z4ai9kqH(6gegj`#>jOY~sTJ9{TT08<%a3#-!k??w=!@!^tsF$CO<QbT^k&*Byl82i
zT|$%UJ8qzg`l%AA(lM#O^qsDij>oF`P}I1avI2T%74&AcPpAXfAY~7#7SI|menpIH
zhw7eoT;aN%d_BRv+#orHygz<(ZO+=<b5|by1K%-K&Y_ZpNUz*jb;v&eY~&Wu!7rC2
z*{$)xCK19>yYXn4&}JE-5?<p8ulxxy+DSM2H1vK)f_UE{#i0n2ig*ziV4dIJLK2o9
zjq+3!?jQ6;BtBF`iSouc{j++4&2MAc_Adh5!`tcsXFB!o*<ScPOePRye8sABFh;E9
zlLZMN`95c~?uNK?yAM9$NCV=hV|dKvpQNtYHd#g60dbi!*qk^Seqv-G_P$V&>dJBf
z?lazbWn?0ay#td*PhNC@n%FOz&wmC@WV|muB<mEl&}?UW%dsqVGizl;B-p+S8kM>{
zxAN#KwO|?B@Z}5YFRiX8fN<w)mS*2p*&14iqBhix^Ug8;jDPigBxDDxSYD^WiAYHH
zM`<CX+P=`}^bi$5Z)<<L!C3{dxQ28Jq;pL5FFbiB`iRGLfN1b2YikgWHdgx^nn^0x
z4YJ{~tZjc&neBG8WJh!b@V=%jw6@Qhr`EX!JXc<TIRr(6@l*cYzjO<WSv8yC3;qzB
zeN>w-KzBCt1GLKFSBCqh8b<1*ggRheXH$t6Za--xHgB~ULfLiS_}lN112;q2^=sA1
z|C-Xa#eK^-d`{_2NP;1E_>t%yD`ghw+;`mZ(lv#{@+hg1SLo7%h9!MCq$W8(^N|3=
zhY;Z~MTBtcXIbzU*q`;WLmLpDhyF|+7&VblxLIwML`q{1ctNrH&>}dX(wf%=_?U?o
znSIWUl<0ZAwJAXDz;vzP>t5H?&4^FeMPw1F@2DpTsRu#+uj%aXb$&nH>#vw6;cq5S
z^$qKym1z2mC<9r)KnJ$!F?aPD$}lLcx5t;AL&=T;WL+7E(yT)=HRl$RwJcAl9Q*pr
z@?2g4W^FE9FazAFvIF+ofsjp~h{P1$>RV^`1(WutNAq8P-LM7Z(Ow%N8iGHK(SNs*
zI)4GIbcqpQNU%Um4Rv<1%&wn6U2arxB<0YrzppL2a#Z+)IIoX&Y<*lUt;zwtUrO2P
z|ME-3WN!`c`U}Q9)p^reRcVHPn25sAn;3M$wFfu+F>FdqX`(W{O|UtYI<=KqG8~~C
z6btf&5$zGzK6;#@iXcA)Q9w&#_(e;rI@rQc13^>8h8GK#EkfnbqDpb3$0G}aCoYJ!
zyFw6B(#B=--dzwb*>gUUKT-MVCwf6Ltg$h8<essbZsj6OgEgvrww7)rVu@q@Gn*($
z3S2>P1!d4BHZO700bmo;DUrQ#Dyp;A2QKzBuy&We%nlo$s=2jc&8Yiv<(hmsYkB(&
z(0-dLbZqJj^jVkvmJQU*jdLiX!3)S{(ej1i7c7s;q48zaB1{dRavC6brFQ~7Yv$du
z(jR<+CK}R~wCbRguL%KRO)#)JS=^^=ccQSC9JHFz5<iR}U6_9-f-*j%x`FfeFeA8I
zroeG=i&-dJ%P03^x_U`Wc4NJO8!Yl5Q$C8MRi}GS05K{P+dOl31DnAJcT`cS2^-<a
z%8c#rtjE+%Slg3WD31v;TKN}HtLEWTdB{cG<aHdl%oA}Dn-}Lst3tH0C8O+Jk9`(>
zbtK`icVtcOMWTWC;7}LYc%u3x&;31!u~N9mkcSh>?c;XI(4ehF&lcJx?73!x1$pM7
z|EBnwOc0?ttLPDUJ5bMS1D>h67=(`l)x*wvZKsD!qOmWyuot4&)Kbh#Z-OJ$g-MFJ
zg-qJ((TSxU+sbuz`I_P*8(C;VVvH9!ZSz3#l+T2|0|S+Pbm}O<Ts>!IYHO#13-{8V
zJuCzZwb~U#;ry@1MrzRTIsru3Mq7Z9Q(jVOpc_%9XfF_?E<K@Nz-Nz5EZZKeKJ6{t
z*vE0BOu!N{p$3O0M{eo+LsSbh>=($^;NBnsy=F!`UrL9Y$B6$QNc@gC$^L?bJZOhT
z4Dec$yReV}OO-_A;<z`<+4eUk@Ij4g<XFltUX}=BPD6dsN#Et$choPy8Cl$F6>AA-
zAl&$jK91Z^2ePmHaKb}a4^{ne2<P5C+C_(?pBn-o{s}=D1$#XxMd)faeLr1MH<)h>
z&iI%l$Z4=VBzwQCsfi#F8w|3;VxE<$Y_tybA%OQ=i^1kDZsQ?SK$xJ-rowDLg`ped
zYR;gfS4mpEsdzpt@kiSY9k`m5!i|r3?R+f!Wu6gkG+|l?z)n_`7+i(Oq!-E)nw{W0
zr%pV<jl*Gl#0eZ3mwXhG-ITO7t)2*Cy3hhQ0`c;}03;-H_rQIu$+)yt+G=+v*Kv@;
zWW&dq$<Y~sjT{>310mhb)B+J=)|~<n(7+=14-ZZZK;<!;Dt`rUsTvZ%zDY|aod(s=
zBMM3niegU=0N!I|Khm6y=F`4N%fcw+M$fX#!JjQ2?Z@TAypug^*>ZK&w0Zl9cT`q%
zedpVZVQ_sn(}`?iID9gu>~-bo>7>d%eSELpn>wV*r}^01ihzRI9Oq;;8Zi;u6Zctd
zhT18|GxAuM;Kz^%AKVOW#pr24iGxx<uU3ZEpPC?P((3RLgi36BFjua#{bqE(G}B<V
zbJqB%`LxSm*B$McOIFYsuC24)h;#jxo2{i1c`?1vMzwouN38}WMSOe$(%V*I-Wv^E
zSx$ukJqGe*r2{S{Z00%dI?&p5&%53um+2fnMjWy0kFyECiSi~0u6!45z0o8&3#}PM
zPFZ!ZU!~y1g#sY4RPrjyzr^9-m2rQda>Zrn`AL>#rQ-!s{AgZ;-pNm-Y9Jvv38aS5
zj0mU9;m2zPrJ(%1gPh2?vp6#LO7Sqd;$>{@G7oMU*ACtgMlIl$a{h`~8h#?3p*i!E
z7riyFK;?Gu4QZx7U}-3rzMMq($D#y%8~HK2ADoCqx+h|HK(aoxd3j<C#)|EY^*5+K
z5ySr#Lf~!0el*v{MlcHBcaN`MA7Z8{M7aaG)mgJ&q!6a<Zo#3*Q$5hfsJ5>DdMq1a
zn^f^sp~H%#>580-t{_Mo#eo<vg{pMD4iSkioTT1SzIuKLvUCbqu)PhX#%3-6uz^;s
zn@{yA5oua;q?o@m{k(sA&2MlrQN#yDEGZMRpN$$hV<3hq0^i}9aM^$E`lv^zy;-#~
z!sX_Ell4hlDayCtvcubJ><Ns~mf>Hk$gsK7SjDcF1QvFfXA@^Ukg#;lQ1`XQbmZTv
z&%WW^98_Y>tvfv&ZX&0AY2pBV3lb$*$6@fvU7*Q~@TM8`b1>Y^gbHz0E4lEo35_%L
z`B2jZHCCf$JTwM_DSR_ULe@qGh@c&S*}J5LGC0Zi5C*^W8tAv0PzW-}wOQU5Y2mWO
z7Y6qYPPo~p$Mnf`*tABP-`BQrWffCc46+;p!=28oqG&$u7e#@Sc+^!yRVy)mIV!M^
z;^=jTpPKqLe}IXxtGOuv!&saxvP<*Z-NNf{r=n#Tu=HzBGqkHtNSc9u{i)W{J}<DC
zdAFZ=At9{HKeViMD!lG&tvV|(2I2LJOXXQAPNMO9RsU92X(GGS)IjKS5`WRWL8dC$
zES@Lp8o}j2$@OsinC!oeHv`&_;lJejxw5)e9gHTgviF>RIf-j|O0y62MHpmAXM8NO
zZD3`UkZuezWprv~1(%X^oj0sRHqc34k-m$pikgNcWKl4;-BmqU+pwCS14i#Q=Djc*
z@odiQHGO-?=wJCJUJp^Ag_2@|v?w&Q=EhN>aa#*#tGrPQKeOEy+|W%0@R2c%Vl!lu
znXR8%aiBu_VttOQ#zRvXN=moMyS<#qqe6!gNjNY#M=@<I$Vy1Ajpc5IX+dGcxIc>l
z9eJ#;voaCf=LDnsfTE)L2#mW6<tj6Aaz1vq|DvuEio$5DCASjyZDm$f<&<;fUcm${
zT8BNy8~eT5UoUx43IZ`y;(X*69{=&ka5ACIlYQE(Z13D$q~|Yh?`|fD;{JlJnLt6C
zK+=zpzRqUmZQ7U7LxUx-|2&+Y8CNt>SV>;KG*gx~dLpY#6zr2~GLieN26~KS&?$z-
z9q6rQ7mbdezlN-pHBj`L=bYIrU=hm6EYOwnZruju08gEhD%pusGDixQMK_&rX7Wf^
z0F)Zs*f?*C+X2KWroC4xM2l`crI0j!o>lzgW{7@o_fhv^^bLWVYXX^$qXkB##}LJO
zM~evKqL<b)YF3KvgKUwkX=-#%9%=vOu~xX~Eahv~hIwU;dzWq^@b3d6_<ueivi}ue
z;y{=Kf)IG>dWI2)sGpZmodZ~{%;2E5C09Lg%rs@%dv4GLYXHsj>-4KhzY2clgYd~M
KX5P6R^#6Z58+J?p

literal 0
HcmV?d00001

diff --git a/public/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/large-result-1.avif b/public/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/large-result-1.avif
new file mode 100644
index 0000000000000000000000000000000000000000..600133f28ca83b812005fe278fcda9f25cac9c01
GIT binary patch
literal 24495
zcmYJZQ*<c6(li>|wr$(CZQHhO+sTe?+qRwT*mnMX&Ueqc{V-i!)!nnEA7=&t0076#
z#nZvi&C(3upV?ZPG5!bJS{nX~0Uc~jT@3%j|1&}h6C0=hg8%>ymc}ms!~gRvmNxdr
z{|Q6{0Du4h|APQi{?7vd=x+Vv{+Yi0{}=#)e<6pZrQLrw{;!JqFEIUY+P`W;HwMQ4
zRQ&(sKjnWUoP(u{?SIt&cB`{=aB}#Eqbwba?f-cgLpMg@KoFS!=qQ%X4yOMB002Ob
z|I#o10R#t256k~Bzz`4+|J?tVkMkeuU+4eR0RN+K7`l20|A%&Uvg5LKFg5@0BE)5E
zY3#t|?CI=cYU|APFUQix($LA1%h1Nb;=l94wREuk-{Ahu0yq!|7yt+)1PC<jUoR|6
zC)58s^uO)?72EuGT>p-&M{H|i=z;_Q1%*jQPcJP3!h(gsG|>ep!{gy$rZhKGCWAIf
zD*y5bs>Bm6Qb_{MU~EngAG$tZc##uVi%Fxv|AvZ&wC@GLz_2<;cFQqOj`b0>e#g(2
zt&gx{AH;L%=!KR8sNDO=@ED|ebI5F2(a)+V_jCb;Wlb&C-ScWDr|{A`P2lOs_&5{%
z$SRx-OT=!2LsHiwf6~=R31I#4T87oIVSwMc0$g}drl(1U6TD`goB}c>#^Y6EJ~|j{
zXnuOE(Vs_w;r}L<?&BKQVDP$?zeY*0Fdpr1{I0O;IakXGgn~5D?<IA16IAM4`>`vt
z@Q2{s{m|($Ak{lD=V)DIb&@<tANwcA0JGzM*{Kvld?9O};zy*vpZ6O{++2iOg^8O4
zk7C3y=8gC|qG%2yraK-(8#}TU3aMshq+7>IGk?}*MMDXOMrUimcMo{ff8Li#Ow42j
zWYJLH>Q#{^i&7dK@ra4b11*o9iX?n77=#PIk7aVMB=V)?XHVgDZHJljdZ>Iaa0*;n
z3HnZ?a1zdu)H;m#h@LDd%_faYq?X7zgal11e{KK^KmyIPgN8$9(cWVb-p$0z*^Eni
zZJM0S;vxRoMNAhTg@piZ1_vi=OBQ2{l&AkjyT5pUj=*OJsfRt=G~t4iHa&Ojd0AH;
z+7bxFkodaRb{gv*fcYs?P0LL}(*?a`y;Jw{TjmlDB$tC7NfBnN9oXRR*Ws>P3rD*(
zs*e;$c9wzOD$KT$lgP$B=>=7yam(WV6LaRH{g!-6mU>$PC>#jc{ty|B1B+jJpo@{y
z3W-0l2CGFZ`rhdsUl3pTrDe`YEK$%M2!(MXf)AOy@iM~$lQGUbMCF|Zw@*#8D?ZJ$
zAJ2*UMLD*DAv0U{tZt0W=+$&lFxeuJ?uz7dg@(r}{CK^8tU)Y_(b1J14Rv1d^N9R#
zFuSb$uPUAB_eZ*`cqY+=u#N~Y`$$IEqyTkRnBzvnweRSw=SVNZ5EZSs;Jw$tbZiun
z;Zv90i22l*Z3IK3M+p=bKYT3`xzou$Llz2$<4WdS55~1ex9(qeKm_RL;$3QmbHG2x
z9TKjOUt^arLj(`4BK*0WU%5WW0e9rzSw>eT3mKZZPbsyx^U7rsmLKwAMXD$gukRD;
zgggNK>|G^j__?`zWMc!{6i+*JuEg0;Y4Ic*6VzF_3k^0sXN4xiocP9-v{LIa+&=;U
zY|{ipp**`o!AZ4-;_p320hyb`606;`DjeBBq2v}%Pw{P8f+(zbP{o{h5aIs5nj*by
z?z=_gH?ud=%bfc$>LT3K;8bgk)&(s&I^2b47tIF=Pj4l8o^a`r79KadRXQj;xYC$>
z_Y|fr7ZTX*!_jwudAgwh%MDS+Vyy#0Pkp_=DjsM-^P7<Y^Ay5->p>|;XZCbTaIku3
z)n(->zKFO+Jzni$c+xIlh<XuH>(-nn1D@M&A;YA3_xx&$c7MT>Br?FsN>|S^A;~iU
z3;9X+v=n~|C03cS<(?8?a{^oSJ}J5#*AMhkxw*{a#PA)qOFpHWq+Tp{9Mt9KTQ{^N
zaP(rhw=07M(0%Wj|4xW@5bl)BSb$#r0Qi>-1UKSms7Q1fDe!7SPKpgiFu@tr+IW$u
z<}atHA><H#-_7Jzha{&EbWEin41zzH3papAj34Jq6`agNg@gVS@o5w^5<BF#pk4bD
z>YxddbP!fliSFj!%<jhKi~5SVr1f?bOA@SaUr<Jw(z?^b)q7&Mc=1{L286R*I@FxF
zO!Iqq!{L&@s6&67Z?Cjg56FDHd5&*p7s)yGf{6R4!I+_#W^R2SSRl<icWYS(ijN3p
zv-zH<I)<gu{TR|Bha2*#z^ncgJ>ZscY4hRoSU;)A!0~qN(jmOrD*=3_=pou(;@rO@
z7tpQkRRtOe%=f%d34NF8kv7?Pns(PR+s;HyC{S5j(|<$m6j%W>atm!*f1VC?@w8Q1
zXO9E=f*JoHdAEqBFXVAyBB0&7m7*t!8-?cOgmr5+6w6^B+4A&k_>5a)@YuT9m={bD
zCq}E2Sd8U85kX>%9OL6%B>w1Ea1aA8lR-9dpC`#0LDKaSoUK^DbyW!u5}(uEzuuMP
z!tiTFinClKAMw2iEBi4XBp!Gyw;0+AM8q?{)npIo^iXhk44sPKY^DAwoxhSfY+B*9
zoKsCTgXK*EW6&G^w9&6PlRPS_&;}95$NoHB35ag-(?A;;#R3lQ?NMuc^s(k{hDnW8
ztdwE9goFIymA0>c4#%iOQK!kee@%GJyqWe+x)O({#CAbMfO2dvIoDuuchewYWJXRc
z*b53B6-TabGc-0s*z!bV?(_e8h9#21HY&RXsHWWN_gcca21x3g3A`0FIoB$3>FeIa
z8ZGlw!e!d2sucrf*Ypz83Q&{%$S+5u4J3vs{2K6m%X86u2ZaUc#&#%U3w?J~o(3Me
zlm`^nwoi6~{pre48wmS2xtwOtDm#*I=T`N{*VRJWO}agS>z#e8=G9MQP_!xn$+yoq
za~iOr0H3TWsv#Z5SQ$|6S)Q+=qe#D#D?Wk8Xo}~X?89Qd^I%vzR{5BTqQO99=2Gv>
zgm1>-R?B*U8OJ*oV+N>jvIvFygN&{YT4J}$s^hDFk3BHghlnZ2yT7TojB^;az9c#~
za*Sy21E`rfF(*d~)6ylF27EbU^Z8vn$l!@rV}`F^=*i0=y^&)R*lD<|!l$>Y&GdGB
zDieHOlrMY}TLi;f3H6kQDrD*l!!eRhHr?<#8Vhm-h|PbWYxRq-_{<9wXmFs(1~HCN
z44BYYP=pa0Eif9+(E$(W#|XGH-MEz&pl-l=OviL?g}A!`o0PmQbsn5nu>8bf&(T@e
zT&D-NKHa-OG?PuE0>j)<bauMi6sl8g^p|-Cg-Mxht{~nAQvgkR(dd@vIO!_Z6DC(1
z;dybA8WH)y=M$s(@}?$!jDn^QebRw<1s5F+K^iL5Dt5Lfw6>_Fwd4v1I^HMJbV*C}
zak99deCKS|NA%WCqvM1>nBU%-LMwMQwp=Lz>AU;dQ%LF<&Ox5qgXnFNcM&f@4jci8
zRL$uWyV{+qtKzaQi6!EGn?2F<f)lgnz7OrLon3`Z5B!GQqg|W>wSH=f=;QV#a_qI(
zZXs-Ao5N4$hU_tYmt&kdtz~a(#Z`XVx)R`hAI<X7;kL$8ss{yS7s!=A-21nmS_#Uh
zfdlWF8Tf87Lb~2<x}e8&Oq;Ofu>>u}Prd?5YMTS*e7R!OixK<HOHT<D5ddC|SymsI
z)1)c%T$kA)?!}`YUL77Or8lJ=MG1)tHuCWep1=}$SyjHyJL_SyVeD7=^3>4=Qnsnf
zAFr!wA#F_>jV6wfHu|bP`au1~)O#cap}(9IPd2=#z)c`m8i)3es`FTHTbr?rco$Fv
zSVb+acEVh>dX;{ZG!MNq%ohS}k%ungn<sxsK)BkF>jHUb38&;vh%&*=;QK(Jbvq~R
zh9%T`fTyqVYR>k~@IjspIb&3~KrjBl1pIyq`cp|RiSnpN%+z<-8bAj7W^Ba!Spu^~
z>`oDXI*Z}ZIc);Qt(q}Qr|8tf{zQ?!RvoLEBM^L;zoQsFXrt$;Po0{pQ03_ZV&|IZ
zw@iqu#H5gGMD77e_oiU>mjt+!=;4iX4y=>Dll5<ygZ^dymFAnxQ|A_}viJ)tgX(Pm
z)Rg-A#f?3VFc7m(>PwD_@?JQeax`?sVq_h+XO+>7#A8!X3OqNF2sC6F?h<!!;S7Nl
z%wFK^tvE<8*VB%kv1EUW-9eKZ%|<-ciS8>fpNHps3Gs4gdn@@JsT+o7h)WI)&)oCY
zVL3aW#4NbVVxfJe=}7I#2M&&#EG$px`ua$Z$OD&y0!~r^H2Dl7NGLM;U$`4dLr-5Z
ztoV0?W@Z%Gjih%f%qfVQ5wCD<r~%%0w1IdeMXDA%u3*NNf^A!FYJoSBm98d^s?Q$T
zvAeU}%GJv5ij%^$^Xq-L5ybj4f455%8=r3&sg%OPD!NQ2kqIQt1Gr7d^1n}&OIXCu
z!sy!Q2Up#4HN!!w)NmXR>ICz7v#dOw@(YgIgo1$;#bIL&NHlv@*e=UI5Xt!G)_02n
zV*;%GuTfRKlF87%6^6%6XgY37Yma&XjIkddnwF2<1ym@o6{%W7ITFf0iFDDw4wTYW
zy5BM|A`k()Ua#W;jx)_6&mzOgC{u}+lT;F3y=(o3oe{i&-m8rtZ*u44w>Sb2c*Fq&
z6Rq!zPak>S^RpX8C{=jRlyN4Rd&we0k1WJcR6@|Dq{z{)q?iDSV!<kX(mlQRC?(b2
zTDS$kyK9K*KjJB%kML9`&xXR^>|ROiH4V+PX44oVGNW?x0adI9#x-+gs7~6s{_VpW
zzddoIG%NAEv?OzJzaztp8X*X|io?Xaas$o^EUm)pAEKU$qh$bZ#LjV=@rJ$uWh6h!
z#J&QpIDG1kaaHD9q86b`<K}6cAF~d`Qz9RQy7)@JKs&xq2pPb{QfOxQMS;BZRQQLr
zoR^Fo5(}-#E5|o!Mt&}|-nI)9B^cO+6ip;ey>tj+VRk_hfV1I(7hoh-Qu{4+7528q
z{b8@5woNYev0xX^et$VY*QQ+Afy9G%zz~+RvWo|Q97mXsfB)JhM?3o3!ttvpIt0V>
zonUR?cPp0&0b#=ekizxzkR3H^zYpku`&U;ReZ1qctHsG-XkYJLLzg00-$Pco!+0^`
z3&;<);@CaWfGhy`FJFdYtm|ThzC4PI#%QJ6vf68FX|>u;yFP#Xz3D;^D#q-l;r<c{
zaB6)RRx#NU`TiM6!EzfpMBjQ}NSKJ}eLy3;(>k|Q{BV$xoYY_odDcW3gph$|bVz8(
z)5GX1hVW3KdB})Qq~>4~dxgjdA8};O@2NX?>138hF710=&xGf<Vj{s$Kk?t+S4Lc&
z#F?({A%Cxz%t0-h99@LgZ$h}kjkFO3`ON0G5F0}<D3i_2W+SWj_O_PAP|Hr08_fE;
zfn>224=&hxMJE+@j0prfG#Nrl*X)v!ky%4H$KiK(t*Fa%oAuR2u;gb1K$$#w4~zb>
z5&?~r^8*E~+o%QR?t`Ac&k>ZOSiw$%Ctlu(;@2^AYPj`K-5k+(<E5}zcq69`z$$(X
zZ`z?fT~1o8&i<Y7Uwr#r{_7{9UT1l`zKniY!hSeFyX5S%ao>ye(tyV03pbz7+MX$W
zP$CP<I(BDQj#b*7B5OpO(u$Y=7a4c-h+MixRNs~{P#h(E2CO6-QIB+QTWzSL-7PO%
zF)aybKMNQhJoMIp730PxkFYK@L~*!S94013%Io;=&6(+Qf2(pO3su;&pZ%)nGLB8Z
zyPj$bSK>JVpGEnlcu71t;8^J1a%e6*Ntj5Ywq<TtCp^c>3sb96u>+khPZHzq&&p0w
ztd>vo1-3UV<#WOlY92?Hu?j75e@lzIKKRIKFMy501HvwJkbQ|g`S$4#glb(^8~qd1
z0LGUv<G2i{$0Qt?Fmh<QM;7Gdu~*s;o~Kw$5c!2Ee}Y$5C6|edJ6*02?;&?tW|%;B
z<TAa>g~Il>y#}Q!E0~02l&yYBNU;E)gjo3!!8@5|#JUeF7X5LltdajO%PfE?<MR=P
zzj=LPN!dx-_;(_hygO~R5s5y7O%?k&_)e3)M9xNrf4%P=zzkG8YL<dZveg6^j{$T)
zi1EsIB2i1*TdUHV1zA&6xi$-mN19^P=qjs5gaE=0T4gf(O=mObw(2xo+3_7-iPF*@
zwK%d$G72vzmkSW0=dZl?(Rj$$F{);}UoaB)BDqeY3rCN6jUgd)7gfIs6wogl<iH(n
zS$7uhewoJ~z5rFy26%!A<`($gUx@a=;LUq?Bm&S<WXhQNoS}I6^8ogXFwAc!D#wOC
zDHc}9k5%HtVG+2W1Du(F@LlJg{6=4j@+qYYY*4Y~mCU6%be-@`dcNbaVv1wdS5Hj|
z_a@!2lQ|9}({(#)YgvTr;^Z!9yXjpp9Y^qhk?J1^?QARA=^p=C2*N%-C!Ribm!kXQ
z1o`iSZ=Y5)H?Ou~qht5nIdD1tx#z%IaOk5)sr--&$I}dd8YLw}FcOjF`1`V8Im^eI
z1__g4nTz6v2oh~Syc7(_++-`W`xSuC6Hh^1F*mW5GZA-WYn>$ByUnC&=?rVq#e4$y
zZPwc`FldGytYGJ0CMVqUOmQReiJ@;<lr1u1X0Ag^$XrX>k@sAg<wk@?mrYIf++||w
zW|m0i6SX)tQQT)(xn{|zq9BgEtIz%8woe6p7D!R%pUvxxAd?;r>~<>>9@e;LHig)q
zG`&<JVdk0L8PF*bD6iu2aZtrAy8KQE8{7^<D>JU)=k#6@cm)<%o>L<=M#5o??o=aS
z`yc&A1_n$TVq@jH+dW6_2gO(Y5^7VU4L_{Ujb|(MeBIiCpx^-AR4M<pZcEUO0Hb&g
zu|%&`6L;iSkQprH(jKxScfc#(KbY~=*6ywuGd@}14RKt8o;Q94ihwqeWp`|&WcO)U
zV;!0-p+MiImXI0g9x=idYa-ew)<HU&eh4;Joi916LXm)$pQ*)g?A75r^snBA4!Gtr
z16iqA>ddH|h+*|7jc-12JO?0c;*X$v0W?snMJC-G*F-}NhTo!!I@Dtk#Qi6`YL8(H
zOqnuA{jF%$f?xqW?NZi5RsAZr0WNNR%#O4r4Mwdlm%M6Uh?S^JDH%@q-yLm~<-84e
z*^r4l(IH!uY9>wnJ|CEN{IVBxp8^QY_A41ehxR!ZkMeUdpgaJk4E}<)Nk4g?=h@II
zlb^UnuUS~E1bRi#TFgZy6s+EKCeT}x(6#F1o2)g@u%h>patNSaaxQZIjt8i?9y%G&
zPO}K$OT%wGc<6*kCI%H7-gR~=TZ_6$0wbs<)1j3YOtQb+0vJ;wU&dhOaXQ0HaXmzt
zdd85#?+5PRI9l-=m&`mVV*mh@v&-z4F;8xGEgvt-PB*vt(kd+3ee|LBBKJ}v#HhD>
zmZckbF~#jo6Z9qD9SS;ptC7U@lII8MSkNiwTV_!YO_3<JWq~EvZuNJuEB@k+6$CP5
zVXRyIjZ|bZa|!sj$^1?mTrPkLS&fB4jyN7VmsD4vBUEJADPnOiB}8ppBWDcfaXtm?
zb>cuMbKY6J>(AVLHh0ix@IC9{Cp2(gP$Nud6dOKhusR)pZf^zoE<$UtXv9HQtKPnJ
znN+dSW{8LhlQ7eO6X4Ksvp|1A&dV%Gc5g${qz051V+3)<=Rw~r?~82(jwz7Yhuyc}
z7TJqXGpbME5Z&$jBLD%6V`>FOL@=GM>&7+wVQN)}<5Az_#=8rDRA|X_PbZb^=~DQE
zS4#^NTmU}LHDd>5Q4;;Taq#Nm{!%VB_GK+s-qJ^P!qK84?;lgXv$Bhil#n4Lr2a-g
zNnEXb<?D2u*I^ohRYe%gM>!xK;kMHiGUn#_QzR;Dpd&QshPrN&&|9;SPDl3-DQXI&
z%?CTHH4?H6&Xm|wOTY0Q&`#nUiY_`Mgt}@$AYf!^RS)j`5rD1^4LX$#?|S4)s3}F3
z)ndQu0ZE}97`F~im24W1VT7FXtw}J;4EmH;1R*$PMA(CikN}3(M}|#@2XU2P9ZJKl
zw)x6iqt@W>20!RmidR)Mc!=-U%G#_^vTxVGseX(HiWd1DbP*6F*#P*<lO4`~-KR-)
zjIjEahA8*!5bpW7D!!Md#Q%)(eYI&PR=gxrb}RQV3)o(>_^jW#SPIy0u4B@sxoG}W
zT$>WgnAc#*o&%UyK^Ks%d182&Y91!usb2Ul<sGt_IEv*fANI)UH;vr*`58qTzh%g<
zN3Ep^FiqG}OSVN~ALc#Jo^~LY+j;6L8Oo4xH#?T(ypVz9r!|u<t0GU3Jy_`o+Gp2U
zq+p>eu<Km7KVXZd9@4n?HvqMFVAMpZo*Kf7mB6xZFNi0YWJbNB)*1-od!A_cJuTB6
z>9<3ao}4gbfRuS(>naQaM{5P+Q&s;DgKw&fNfq>UaP8a>_{<_Fj^GV`#7}@nR&z%6
zjw`OSWWfu0L<x#wt2-mHq(<`!j%xPLEN(A~d@ZG^kNZcXEdSl{H^&x>Wm}qhG6h3{
z;D>^%z45G5DnLm@Oyoea2vGf;c1ZqaGl>vI5-6T|TC}8j<4>{IloF}h<l)avs`&N{
zp=s3djt?M6uJ?mJk@e+=mvriY2ZLs<Bqp)2%#aXoi@!DyM7}zxCI@<m>q4%<<&nXa
z&!FoQ7q3#iSl*@;S^A2Wf$(yctcCF;*F{(CEG#RDGz53R$Fo$-tA3*Z0oxo`7ul4P
zX2w2<+l50E;4Ug4e4Q$LXB0SUAdw)egaR6v`d?aCGxVuJRy}i;UH-2$4rcs(ZSa1O
ziCf;~Qi|aG{fHIRR?-nC<{=j3B=$NLE$uSS4elu(xnGXsc?lwpD=4!fJ_7-FtZ^&W
zF7=Z4zLoQ#Zp5l11LLnOkWOC%StKTb>aE-$Q}p;<<*;rEj3%RBGP9aCiYQ$@kV(Ol
z*1vY1;iDTJ#o0mdBD$iQSX_V|dhBH<!;~)xno<|q7H>nwYsD|}5)s`H_TYJlGO&+<
zDX=btEXMqkj?S&JtP0Q-VpIJvC1O9yA9JKOO){oAPZPbV6B>Og*4NH>#oPs8E8mNx
zN^-5<r|je*O>8I(?*}GKh<;<(%YqO*S?5XmzZe8Jh+da|YSJFHW9zHo*NC(a^(O;N
zKz&h6!&+jZBo`0NSJAg!$+n<H(4f0n4qn?>D*TR0{K$sV-xMug#)vMQ+94P`YA-nB
z-^~6zG0OL!+^FT$nOWd;27M<RmADuV>-?s8g6HlHs4>FH1uIBc6Wy)y*q=LpI&o^-
zBPJTbb1Bgp;51VZw`x3PvurT+dHVduk!z~bh!x|7<px3Mz>~&B<HTv5E<9fHi=FWh
zz4-<6@#dFjBkzcAMC7#medSeJ9t@oVzu5D8&}Xg<d!5IyYPnBAb46>7=UV)FqHtS8
z^zG?=a{LpL>5`H&1${630)};i2MpOAp{!~>UPKuP8YX=COFPC<N}Xm(dWHpA)L@hX
zy*vPAZA&XL0m*}ux$#V2n=l0ozeW~P=gT5GXJWb+l~kpp$nc%Zmj7y|Xe7KjzA8T(
zw5gr`QLx5iAdCML$6z$77hR^8Hc})mCAr>2IkO6ONo&a+iV-t^J(yPvtNWCC+RWyB
z6SD(|+`ReRgEMZ(p@@oz8`l~vG0S^|g&Up)v^uSZI%xg7=iE|esckuA-Sk2cDj$cX
z=Q{M)g;P4P2|<6#>jkO80tvhgR%Zf?h&&t7x7~F!S&E;D9Ju+_c-x_Q1xbmz!GODf
z#>2PmkxsbmAb8}O1~E92JI4=!`xBhb4}i}wYqYFR)n}ka>JXlmyz{xXnv=m7MeKRd
zUM?iATAs*<K@Ufc*t3sanrID1Bq2wg5cuz7>Rcv4xU>Ffm@)8CHg137K&Ua|u}&^q
z;Wf>MpZPs0m1{PN{AvDH=2(~pC8J~i!G{23YWqO>;(lTyuzPr><o&{1p|H}Sl9Ne*
zM=*)NHAl!T60|Cha1Z-i>1F`1H_0)EI}#@|n-%}`!DBX}!5W+j___?26*w}q1_&Gk
ziIIV$`tf(;V&YHaUyDU`SNlX(ApRTWoy&sFW>Rx1YFssv-b69{gs96|hV-Z(R}wUl
zsZ@Wb){;_#Yh1Sy^XyWX@)i4`H;}NijiXfl@pN49MJ{vJr<3*ApxFd3R{rnw9>78C
ztF1^gt-0hMcKyqH8)91&2N50cza%ACz2K5)hHQ#(_E@@zv-ct*m+_xWvxURex;ZR=
z3J25VL+FcyRaN%CDYW*;plZAFL{f!V*6zlqw9s+7kqozTD7D2G9x2woktOZs(->Rf
z3U=1cO_etwjKT`>{XXU~P+kR>n!Vo@hRuXczPPX}PMi0v_|(T_{Bwm;3~R$%839Tu
z(ciWqY?2_j3E?*Ktt#3ed-jLI*6?5@>hV7O38uCyd`MyQ{Nm(1^AN}TSa*+cf)?6~
zHx+pUUy|!s7NLrj)^H57$uV@TxG=l?h{(2U<+jVc%8@RXWS25*OM7;eDnF^ueaz()
z0Iq<hlaV=@bBd`-BzUHnwG?BIXDFuU!Bl{bZ6IIwlRC)Qxta%zYG~6(hb6iPw7hUa
zUGmMN1q*3v0~|aHf*yP>c_s?UW1*eB=OLU~qM>zW@JYlbsd)v4UrC+`L#yAs<3b>^
zdZ=0b@%Z=Kgrz3UOHC^eGYv!OUc@%DT@u&W^*0mhAPHl)$Z2%P*lAel^vH>pizj7)
zBc3<`x;Sm#FjNjJ4c5>IlP;Lan>)Nw2F)8UsUm!kJiw&pv9szO@*~lw++nN?n+%D}
zLu`7;7m~XCOsa5|g}sKZktYgjx%$r!J<CBZFJ`&QT&m)+5B55Az#}s@g-pC`A65K=
z9lJ>?XA;i*eBm_+ojmR2Kn?l+UBTJxU|*?E=9hTJdcpH++Xdp<_U%rv^Q1sRbTy@I
z^|T=`O7+rsN8rGG@M^h^9-c-(n}Te-dXz5FD?NtxyD17QksxES=P>lkE59n}n6wVJ
zJPwC~yl_nI=gaoEP3e~}eZUc7F#WkYQt2CCcQEHl9nPQ4izP(kuM^HF7!2FC$RowT
zTgeBrZH`Lv66kaxvtC{8U7+jITU(&y#RF!$O`+hbpab)HY|i;b?zP=5i0LZ0K}xo@
z<^Q?Khds1kn&j>tG|QEN*4c7u)2lQQGw4{)75%E`S`Iig`2O-|?{ko^_DI9|QJO9P
z=1P^=ka^PnIZ|RcY;x0dd=8<YsYK>&#}>m0v<S>=Z^vfDl_*!5Q7yWf2RUa1Nu|Xe
z?->p?(LKbID?eg!uDCUhk_3FS#NG8%v2E4x!I!$aHl-17%ez}{HVJdFn&e#c{ds$9
zlW?B%z_Up`J(*~#D%a8`S=WJ3r!^<tK5@B)L5`2n8Q<<3X}r|e6@g6XN%V>f*mARd
zb;@Xa4j4UbgCpMOwBQx!Bt^2EgoMVsqQTvlW|z3guRRgl7nsrR%kbjTRe7ivTZLb}
zlp;A%`HMDuomr(3yf_F!^(L{rW*e(LfN-<HN->2Lt%Q`RLxKnemVNC(jqKgEGa0lU
z8SF#pN>o;)X;k^yB+HUog2rQSN4b*0I64G=R-l!U4-qVxuMM5)U6q>4vcOdw=B^H2
zP5>iF$@UIxz!cTH(6Z`ifL-#TfnJczk*;=k@&S%;3uW0^4L$70VdjfKCYDcPCgJ+G
znF}hs-c>29lu8mRe41Nt)mNdQRID*(|B=Z;d$Mtlh$_GW2U%Mm+hS5E17?*2B`Sg(
zZOa~~&T(o{U)BkV$Erg-WZT2Lkv0XCz{Z(sL;BK>NYd<&{1~HbS6V~NRs*7AD7k`J
zzKi2)K5|a;^I}$G{#1(M1a#yQ=q{N87+rp7f?&KmndYf(MpK@C4$o>W>$y`SCuoiJ
zvP>wlOtK7)LR}5aJDl?0*hTpG_#{k<S;eP~z&vk6uiXGw!j+)IUM|fw{3y*rE<=}U
z2?n`vL>LrsjnWDuQ`9DEyer$XmVFUqIoRqAE4DbU6+B31O`0Vw5d6g@L;2IeBkxsq
z()svg5W%|S7z3mW-Fnx{1U1F+Txj5Dms!zkknAAiGSQ|&9tJ8xaCT-Fvo$CP!F<^y
zhm^DvUuP6gZeIVg37FXAKdxtv``_>bC+k@q=9%eZImW*Zpi0*f%<O&9jM96IQk5l8
z2NEhFqi--gkOC^iAe@1M2{m{!6G$l8G04x?b1n)bd`G7NO=~+aDXTkkRqFaY@9>!h
zs7FQ~TVi|@{Yk=bV{tY8EP(Am%!Pl?xaaOAMIF~9Fpg3z{Sd;MS^aEf;gjf!El_fL
z#cB>~F;fU63DTHtTdih<4~%xRbJ-mda=cH2T@@`WPFqd91=aABVdq&YCOD{6%v7VS
z&}Qc2;Q)&J1>Tz5<e1qeL<)B3p>E2KBD2(R`k~xT!MtPX2)x)NZ*)xbYdRahx1cJO
z>79Xy1C0$hLP^ouB4Tb=`Z!$Om+=pmNB5>Z`8JLo_605&`kYU4G^p7HTWW7YhJN8e
z#y$}NR6oz3&B@IgZGj8d;tjUH<p7Nj=}C})h8&{Y&8Uio6TAxD8hI{$z0HNlh@QL5
zRX+D%^P-3AA)COL7bB;gzxA*H@rpsjEvz3xT&;jLC?DyQ@wl21qD6Un8Hn?{$j-ll
z5wm{MD9<Pq6_#a5Fjz4{LLxzueWgF>n0NrO;HyAvuxE{*ueL~jF8Sj(hyP$8+GoT%
zGvVHd)E`<5OJ_a6+hGISfujr$tlf?o`}<sZ!LYT*qTrHzYH`=a2qzFL-Q<pGb@*g>
zLoB;5yjG@6_a~Cxj*`cYOAGuweMQR0p>!;_B+Di%d+xf*y}lr7H8K_ll%TB>{gLKf
z`~{CUEih-MWQg@LBQF$KAJ2bYv*LrkBzm_gm$zW*^IGRJAy;4etJK6Po|*YYQS&I@
zz2x$7SXb9yMz|Utf%b{K*Slg}Pyz)kfS&}=55rI<$Q>mw%mJ9N!y0W#YOV4yS5&j=
zDz$(idrhtaM%ry5$DkDRsBWIlrow-bLezUrk^Q~uLx>iD<5tj6ViR$8y|Yw4U@)gI
zS!+cKlr6>=LNLutaE~lS{G>l6@{YOZ6wILY2E7Q_Z-c=hYD6nE7n*8CenI@H6&U%D
zVV$W(z!r$D(RGd9q1ux`zTlA%ED$1;zd8z9<|86?k4@Fnl@HRro!*=|tw2no5;H^T
z)Z0ntJ(~vA_E=@{hF!`uW5`mHb=XdTm}<8UY!B>NM<9U~_Kw9;1EOmhfMWcv)+d7K
z({%YuPXXEY$Kai7=JEw{b-9ILfbT1P{J#A{v#0%h=MF?!3g`qLTj>5Ry)OgcYAGpe
zEV*ZJNP&mwHN=X2h+0#~&6xV&%{gWG9A+F90^f7|<o12J8J&^Vy?_MQGb@k5E!%sC
z3qy8;s|XAOlYj`<GH>l}y%+c(v^_dq-rX9B?bFL^E5oI}+Z+hvo)8;Kx1=3va%SHg
zKm$ilZEwBcF>ph4%pV<IKGVd`UAyY}5gkXx#(OG$A7N()m08sOnO9+vJ`PaDVZbp?
z^Q8C+snlwX6AiL%^yZU_Q$nQl4HkCVTc4e$`;QJxD1IHeav}6B#|jKsGL^jg*(=kp
zeI{QXZr;{1f%4e(rws@3(hm*;bWCrJYcD<M$}4EKw!RgU0ov5id)+JRB2c0tpHB8G
zd;u19Bpcql8VswwgCd&iH9-0lC}Q|c$-{da>U!}q)7r^uSlIjna`$fnpt!4z+yu)F
zJe<kxZzO-z39z-C&Ae=Yp+|eE#V0w?LKGr4{f6{h1u(_2W60s%nrLSe6;B)8bS^Y(
z7OyBen#evYZE9uuMOd}>k2>yRm9!RK5}qW`t%0)QO%aI@#+YMAdP7N`)(o39@-4pN
z$az`#IU53wQ&e$CVZ+pC>hoU2K{Jnb!vJ1jNb#2xTzsZb4KGjC%wCogW$j{pQDMiO
zo9ohN7;CU%25#0BfrQcnhlR#doNJH!wz?A4UiqGo9f?0_yk<2>UVc)jy&}m^7pjYh
zKIcqvLbv2#RI|poj2*@h`S!~rh7vfwu&pl6)JpDHJZt-!)tkQ4j<PU<TFBwNQ#<4b
zh8nL*uCV{u?qQ*3&+k{2anu8}v@9U_82f@UMd?)SJLjk&N?=c7|D^+ux#_aN7v_=3
z2+)W0AauRz1s@R*NbFMWL+gn4T1@^y1thBf3Yu7nIs2>)y-K%Gkn?P6K!k~KQ?q{6
z#6_BdPN&<b)LuC7!_AE>J7k&v+<v+a?*T%7B{=*A#8fZ#?7QM+yTASg57(D_4rvOm
z$FE)vPBgi*LqCc5WSuwm_=IhkR~8q>M`TB8+SRC!bvnt*;388=o?_=Q0}S*z1%Y;B
zWXV%UjI*n2WUYtf6C}{s`Tlk;JXW?hM;(y!{LMnJG7(}<J0BzKY<{;JJ`MHHc?}(d
z1`b4fRCL|mx^&w47J5-E)y2W(cqf5KGM^(KB+{y9-T`7!7PU4a9LbHH?2coe(nNv5
zNSEfq?DF!f^lGriq2yCK${!jcl5mVV^dKpnqMLA%3RXXdpGm;_qY^@hbzTIdU}gL!
zS|M4`@FtSb69>yHysx_V&NiC72qP9X5z8OKFdd9`<d8xNeV0+3k$6g#T<BFNlSfYT
zNqcGgO*Ien%^DB*IXXSIZ(KPkFD=UQWLg##rAS>rOCT4oNlu#~CLWse16bE;)jQ{O
z$zRJjk8~!I`E2BhL%BgwyB*E1D74$>b>93U{B2E?81DzzhwUx4I-7A9Rg@o$TH-k<
zp{XMKi7vM(FB`rMrpau|9_opNo5Ehg6X3Syt1#HQf(|<o4iQ+5{_p<y<8RAl_(Dh;
z?vCFWadlDq<=~6VSM`06fmddS{Hqa_MPC1i3rZL%mSu>Hm@WdYeHV#Sr)cu3j_1PM
zk(#R<p=2nV=1SMA=ZkkbDMj(MKMG23o{0Cnx|j2>SEPGKXc#1?Ma?v`irD(Fblwng
z{AA%G(=(sy;!5+&)n$bXm=ig!`7(*Ug4Q7!s=7KaF(mzc1K$u98TGKHY{heVw%L)i
zBlY4`_yV8&HP*ElNI%OOwLurwNV^HXf6M{(xq)I*fXz5BsYMnrY+!!{>l&~afT(EK
z$X07AK4e*Dl`c5b+<tS;B>d%64odjzGA)#BKBC+kyt}1dn3m(Cn)7pB(AwO~;&hRQ
z$G*XaZZX;t4Vm#iFIMb7RhE@V`&UT`#g)`K5=g~Gh-G-~?vxqxxyb8?Z<<(v8&VJH
z8zSsWloP`RUe2sY%vO!w+M>wKdq3S!%zNaUG1v%h8&W;ut{xpJl8=KQ2KkCxBjxEV
z!*0bOtJF0hf*zSi0mss;9H%9t^91AHG3g#7SWIgY?giOWJr_IVxD3CPLxJlf(qi!Q
z4q{fXaWaSOWi}88cn!t~Sf*raNgAn^aBr%As)M@%%FEv$;6dp62ZGMQ2bKBSv|$=?
z1<xK)OWc!g%J_gP^pbjbU{{MbhqK?L)2Iyn=U79|$BsL|m%x3FiZzIZ#QW9aBkiN4
zbfYP`gWJ+a9q;fDl5dW_@2r)0aNNE#xQpRy6`p1gs1uh+sulP%_1ee|dN?uG)yerj
zeQ&Cgs7wSh*yk8v*3-(sN&>I_e}<!h@mnVXC$=$x{Hh&w0SiJ>4q)^no!6H(=yG1I
zAX8GN_ok@3r{K!%g|R2ooo-C|?YVT1GqXGpg=8r05tiRAgc@!Ecy@bPYTJrQ2?;0m
zl$XdgtrnZmP6xR}VxaprJJedA%j4uMglRM&1jyLX9{CR*0Si|yY`kvMiO1J$D}R}p
ziwnTfe={sf3E-;c-KI!9#~jxSY_8b<92}C|J;Ux8!*=W-=uLleF&9pJ&(ONkbTnJ<
zn+zDUu-h<3fgM#U;`6Fy6|n(eu)i4qP~<KdG0*uLd9Y^dwMEAYEXQ{<R_qVLboav{
zPo$(INAw9o_)-N6LRmF>5m9|#<*MW(YzaVLiKcRexpEj}e{Rl^w}HK-Fb_S#bdyKK
z9TyXLfd(j@PmNVi_P6c4+`O2w=?1&Wr2N!arpjagaNfCVFImLQDy~s`SdWm8IaqM_
zG3w9T%lPh&LO{5kL@9#@6L%Aa#sDB6yr1mlMb<R}-YX$tT0LzJ^Z+>Ya?61344k;P
zDnE8&eMm;iNOGO-@RTpvVT3XXPvE8;b2xmHRQw<xaEv~GA}V7Bj{7_mTcOwSSq=a*
zHcR|Xmh}Ii`aFRWl$_^_P<59h?HvDmnV%G%@t&MPOtE4pcBwpEAUzHx9Th5=mM1$v
zHMKiXB3>`r`u+?N>(!|xH4zdkzoJAx^5o!Ep#}(XZ44^1zcTWJ6tYV#LjNZzY46;h
z;F&)s{Xviit^cR7$W&5G3>zTx(qdR*Jv{P=18ZaP;i1$>46L+|l>ym!=F!jwfwTWQ
z)cV}bE3J3BIF`QrwyVhJUAhd@l}+Ppk94Dt`l3itz6L@?%FUUxMJMXtEfS`)Ec`w+
zqy8Qb`}fS5a|^>b<7j8{^8t1YEI!xp{yfVObLZrS<BH-Lr7Da;u|%P}VO|&_3yPXQ
zB;^HxGyv|^)mXIL%7Ej|#C?am4g~vL1NECs0{g{~vI{U0i&o3%E;r9FUE&x$HFDLJ
zPUFMV6H^gx5@^O~W^3OM6B?W#-%8?-$Ugg0QJkq*n3@r_#QE@WO36aH?w$>(7l{yB
z`cLo10do+1YDnyRnLOzv_RMEYNbZVZ9dcSmw?X(l-ssA;^K&%)J|Q{rMyN9RKz(yB
z=;b>RrQb`+AD|&x(!;A|7*-@YH7+$C#t#l6|8AZiYKEpUL3BFOa9J|;m4O)lmsyo+
z!sGz`W!M$PnFza6tBVxwyef!1CFE)jMl>|l4mVJ535x;G0&$+Rn{`c;sjv1|2SXAs
zUoNY%baWaYbLw$!bwL|~WQM3pg$`1|H~8Uy4+c-tiPxHYx0QjO_HCu5`g^AdP%=u6
zY|wOmcXt^j+<QOHpT(IPoKT-0CRCx#m@QN#+TV!{dgX5FiuZob?K$tz30*q(@OAaj
z%jzy~nKrH;{1fQ2Hpw?wt<0&{x67(4rR-2eNfZXbEVV73R$)S+ohjjd$!?n19&3>L
zNI0;*F(=fMYD!e71~5Xm>rI7kxV+|XNqh%O<`2W@-vD&_sJZ(bb$pe4m~{LRh62a%
zqA^lTuU3kQbjAuKWT$U1>39h(vx<o{ovEsj2jufS0<@mnMTd*MBF~r^pvkUT9PVlj
z$0i9DUvrcM-?>?epDgC*i<G>zRc(;EJ2{-#GZK%)PWuZ8C*vKybh&;>fg%Noz?0uZ
zI+F#lVrDF6+{uV%Oq-116zc)mac=y+w8@TrEesiTq|m`p!w}qlB+u19R+m<0SzKUd
z9hs0XPVVS9PSTT(SMa24j?Z{Eh>gmdjd@rpEI8Z2<M$jLbP!Ws(EwfjwLPfeinwYn
zJE_109IAXLp?|-Qlo|5;;&0CqtVfcTgFfc4y~<t8Ti%rOWO2#3AH4`%Dhhas7Q%|C
zI`!0xeHjkjFa`|=&zR$E6rnU{N}gD>Lozb|>TuZ2YTM|jl#YT7jpD|m2jt-XoTpuJ
ztkmj^4j9X;h1aKdbS=BwRbSWxSsfq74JSE}iF5ngYPJ`r?}04}xa911CrxBb)3rxD
zlr`7`A&J}L9x~<7G#hn#D0_wURS@ZGKO176(Nq=Kb^hQPCoHjRG*iVntZhh(5X8-6
za@<Nk!PD_F;~e=Cf7lpjrLwp%pHg6`Ut-0xq0Vk%7+B)+6d>;hI&Mm<4WXv3wc{Lk
z*W*h6uB*<l%Cz~*N7?7)o@S~SXN$WO@)AY?H-&uf`8b6Ou_6oCTj60m)5g;Wd<KJ4
zHCxT)Y*A;^o}(97_q8>qck{>5@=?^AfyzRLcfqnB<z&F6Y;fX`%q9Z4V;INNF0M{2
zhA}TP$%ql8>^%T*`1zl(X}c^N!mNS0GoGn&xBSpu)G|~P3#B&LLVQUKMiOra8p-(q
zgl{3D^un)p3tef-b$2GDM_jM`C0#&a;c@Jmv5~hf=e1Zz5GA(j&$F@PAmIcoPOPFM
z4~3VyE(+hJy{(p?(^PLrd3yVRorf4z1IusMwbrx!L!<{mslWvl(iow}G68%(gO~9R
zF2NZo3ikYL0QYy@b3X^?BLB}<hr~oneOXCX4Sl!De&?}OnvY`=?k1-z&A+ecIYJaG
z$l?@??y^LF==v~Z38BgBGMsJ-Aml;|K&nq~EAc2Stzc}n@M-%E5tTEZ{Pl!iwcje_
zR-_4?UX4(vB_Ev1=6Qv$xFKKf#oIOak;DA2I8|#oD7}uWSYJ6<C?vVU&Q57XQFEl{
zMno%$H7eu?l!8W4%=S1`dIOyGUP&uwXG1d%r;ScFgq-6xp=6Ldal&U*nJqL}_()pU
z8swD4nr{{e*+sxn+1@5w<iqVlrbm^FP)2rvMK${ZMqBi^=zl`HbJX(gRhL<^$is42
zC;diVzIeq8*qOOe%OnB?9EI^dK7MGtAqe~*5m$^ck(}O6tt$QQx<2!r{|Xn#+(pO2
z0hi<`O3g5QZ%3lKH+Nd<*+xDV5>j<BFFKH(%HG@ak79nhsQoA@`g|BffBv<<X}QtT
zyqxcorvNU<kaTh~8Yoc-A|-59VlFnCle+Z1Ojv9#J5&ZEGnw1TdIPa!Y@Lcw&M`8u
zB7ESj^OC{QMgCLz;zNiq8XnlfKCRi4XPq~C8oO!AdGQ)Cqc^|XdO)@bQAT3HXjtl&
z_sG?QEjPFZUOezIK_|sC&qb($%bOH@5km{sh`n|dI~dcJg5LldZx$y8?!S0{a`yH^
z-;1=T#jY5E4v^Ud{$?_jJrlwMQ1&;V$eCdb4Yh?mgZhP~{Rm8q<209|W!W3K=mv2~
znG4yy(p`yR$6F!5&;QS^OZXoR($^JjWg5}Y135_N+NLXH`9B;#2m~cZj?N+5KB#RV
z*;m9du#krWeui}Z^|iCo5N5xBmcB(qHymK2r!^C!R9rbEp4G&-y0^oN7C0PRt~FVD
zITL>>G0EauYoc=_khOHFd6RQ#5#V=|kKG?&DRdZRAde}xyhsiBU89wmnq&zZtqyNH
zE2^>os*=S^@Ie)X+u+`i^ru|$sr_x#rQ`6>YdB6L^&7f`7Fy$-;I@VhkhlW0n?SvU
zNS~Bl`oiI_^NF23Lm-btr`@lhYs*$6J0<P4K;<Pg=F^Nzb)HH{esWI4n>>)9hKq!s
zb;*rG9Jv|&z4(L9LWS*rWij9-AXma0WN2{HTSeHd#p{2A;Te{S(?RG{Vq1#4{^JRW
zwtDm13R)4an4?U+miGDTdAoinF&dv-=OeR*O2zWt9oNMEfRDh74L`p7>qhW(d+vi1
zfWEhBW2P3zd=@W)R?@}4QUUUukY%a=yP&EiEMQJtEM_k;#(h7vOrJ|5)i|XmbB@VT
zrdI}Xl&wr60)Jm1Mi+n=^fE{Nkcir?8IqB}onPs=Y_tg}jIIDn`BcQ>|1$tmLz7_*
z-9_Pxqf!r8TPzu^lttd14?W7|&B7;B`tLpK+*b|e==phA5EWTMp6m=ln)~kflm6<0
z3%p^IPzH?{DvBx_0Lp{e6K#Wk0n)<3Bw?yUtrMK}o=-|?99f07DjPM9G#t!4LOL@)
zI{2ujt3caF;MalVBG>alX*i*nXFEQ&vtjy60Jff3*nEw#XB3WL-DIPI3)IluxCwMJ
z9yqK~LYQ3-o55c=N3VUF9+_?p`<za5;g6Pr<r!Csu!MSO3_j(Gcm!WYsfkE}Pz#wa
zVL{&(N?2-Sh=;-#T+(UNxI+$!oKb1re$lrGfzw$&u?pNFkgbqjbB=Zkx+G<H;MC%d
z4OY`%qvf_f@n2WDcEVM%i(^5mAZx$Ur<gkq-!w%f-OW>dC#DU%m2k{<3B0Me&^Qc7
z6RDzwAWq#l`-t8Qj4si?ti&U@YIJ0Acr)EBgK?MrnP96iei#<=q>Bf!`8F_h@x<W~
zg&?z>J;5%?Mz<71l|Zgm<zdD*sYNIyLYC<=S<{LkFIxU~_D(!JovM!v9xyu)q!C9r
zzG<B$LqBz7kC|?Hiz{c^8soPJRcP2i?AjO#1{1sj)~J`1W!N`raYL?)EgCVooMDr3
zFE*MU1K6bff#Y7QS+Ie(YV)c&_&T$Ea@kcG5`{rRx@nZk>@%mv#FBnw%$seLzCjC6
z&c9Y)!0QB#08+Y;v3Q;K&P|hDC0d;FF(DWfzOsJ$*$HypyM47Uj!b}jXoaLw$N=Ei
z&StDY@+4=j@gPN);o5lo%y(>QAFL8WlZ|yCgutJOpC~(#T`P5-O<_@Q25-hk>z~np
zrw30+NtAKjvQ^}%zOEgO!~ec~4tjU|{Co1!HHG@j<!>iQ3uy-8LtDjW|Gl|=3fnW=
zhaa-$_wHt@YfkL?md8#`3nq*7W($x7pQl?mY-dPJu~*ZvWdNp8xG=R`yzL~&pYOc~
zM%Z5BnTr=!TJ*JSmqA$pJXn2I>P=ybKf`uP@ZKP2yE5}-VM?$;XvZ7qftkGB)D{b!
zM*ZQt9o{8RG783Nv8r^vp>)`>uoOCCl_rGrG#>cA_DIBbmlCu%RsUc*2f$>c1i;#@
z(C>~p21gtz_Sh%bOy;L4>zkRuyh5o|^Q*isOZ{ZFTmDS-3RqhbHEsueq*<=8EegkI
zW9DhMRjf!sb^A+dcpeNiPo&`(y;m7-iRrPLc7V61hpB2av&*T_k0ZC6-U`AV56dmp
z?o(_D<=g5q$>QXS`cBs_b(8KCmB;5<<-Wy=-U;lh2B`3Kk?u&iC_HAY(->`{n*v7B
zO^L1DfszB-E3c}-J*<wLk!K8cJb&m4Smjv#jUSpB6CHkTZdXkR1_o>(S|F=EI5(Vk
zPtG*yHR%o*?;j7=cZRL!mR7~kGn@N#<Ie+?m7odPpt#6*#0{o+p0_pbx=N|FuKHTK
zZe7-!*^heh9skm|X#OTqjc+-znb?Qa4^}8Ah%qXiX2l#@dNHsr^W$xU5?#&Yt^G<6
zBZ;z~qSdjx4tL)fgDcoeWw#=bfi*t=T(eh|G1vT+xyj8Wc@D|k)jk}ns16=3*GYw@
zg;vdX_`?P-Uf@$7gyhonWR5PuNoWw_f>DZm3LVTm7W$yT74HM@FYyZT2s6Exw@#f4
zDiTD`>%!m9q})$xXnb`BC6dck4!56LEK!gIlt%%2xL-bo@sv#Lv+BX7F*4nxR^hd6
z;qTd{_+a34)uj*TSnvH2Qlb-KY;ItB!(Drb>N0x7Op62tVNH|6%oAme)ADFo?5jnv
zbK6I~={zPZ%G>B59#kQytrRjh)I<*|kC6z<n?|{l5z04=A@#D)9^x~^UE|>y&tq*d
zzdO`e8}9xbNj{$FrvwW6q59As9=YX7m@+BGH501b4)<IX<{9)RXPn_kr0p4^TYIo!
zc_!j;IAjfYgC6zAk^}?0{W{#?oo`d!Nt&Rf2@jO7*H1%oy?1kC@?>2$M!d#M&0FdM
z`1Zth-QJQec$<)PwkC$5AY)J+@iKzfiLkqQ&4@?N<d_|BGaPeF*H2<!Jr(zRj0INq
zxN0j6l`>?g1w1lOP1ON=Hk7exwHXWRpmO*?<z*efYZ<ybRH)L2^D&_8l5l87yw9@m
zFJx^wbZ-^Cp&Z*yIr>rQJehmRGj;g9=kW%gweIZf=3C4LFu$$15^4P2S>IiBm_`^C
zm$}b6wjAF?4stn)9x;zn`5DsG769M#(I-f5@#QxEzJ$+x^`C3eS=a48Z<tWj3@#Wi
z!6D++^)ionUjArtH3j~!L5=;z#B}PEfK#I`yO#eK@*WN04A3M!o(Sh)PE9J}v(dr&
zjs%LsYiPJ<^`7m_3lB{3RdOE#cQUXlOcXfc>C&c9EtwETrVP@_VrI>Jg->aX(Q2HH
zwoXsaB1$ry`KNRuhtj}XO0_-qsx^ZD!pc939>EB{XADPBmvYeeJ5E|>s<*H<)v=|p
zP0~en%QvC0UdCDa#@1IN%7IRoA353tcGCx)KhbEuv&66229qN>&Koko(K$fH747Oe
zVA{EWhSb#_C+FuDSbJG+iEjg7NU&*rxZ+h@U}Uwn{p%f&zx_)}!3fJb6GJcOcy4y2
z$lUC77Vp&*$v^EvQiX1p&<*_gOrds!Lxl`=i=^p95w#{j-c<R1AdqL{e`zr!HtKGG
z$dF+FPVaWCDlmHJ%Xh2h*u20TCLFj;aC90$$yXrg8&z?Ut_<&2*3qFUG|B`u$lNIZ
zhqO)&cX@u@$awq#7xrHjl<Lb#oO~60r%C|NBZ^7W65t}`Q;4#)T1}~uzVt#mv5hdb
z|7v;Zr!O_h&Msku)ybjPU|1N<fld9brrTr{0NDge+xFJ#^-Z<U9X5kQFC4Ygl8^i6
zao=J(SI#<URDIM(0Ji3OoR%K-a<#dueR8$geFT>l*beSl4n!VyzZcZBwW9_-oQBb7
zdrx!ow$gU2pZ7868D22mp6kN)(sQdfI2G9V<%R>UFybpDdxF^S7ba2_j=iGpf)5V@
zsLYNo!wz&}$|%06Kln5UBM``SUP#q$+YbfgsyfEZZXcbF2PH6%7g|G}T{AdqfO^p&
za~w&bchCWc7wAlMF%MbnDqR(icZtUiX*&9O3tVozHGf!qAdfC5yXOfyK;CXqS}i+O
zB^q;vb@DR!tiHb_u4wfb0!CoFF(h=M?gdpRoX$li%j{<!1lAbXdMsIWEy3UHhB+<v
zCmkfbzrjOr$|_0$f!ZTIF+{vpB@=`Zu3oU{4Gub{M<0I?RXiNJOb<X@(8^i%Wsm|t
zL`E_l(xZmo^BO7#HawE+_uaNKGM*V2=~v1Z6|+b+q8<S*2<IzQs@3Cm0`R_t@V=u8
z|GFD`lg4f%Y<<y5zn^Ja73hWfO{irxJ<1_P9~wi&SG+kl7Cz&)sKdJl#rBj%Ss~k`
zWqh{CDXTH{_7GsE0zFR&*q|0#oxKCpJRP5PSG&d6EVv}u3bRy&Kk>C>S|;>NL8Pqr
z!VnOWte?;D>?KGsX52xU`}hKH^_~ikim^1?M-2SbHI_PkPPi|+y(;VRj$&em5r^O;
zboo;&SQy+tK43RA_7k|*{3>AsE5EtT&NeD25>6HRSm|Pz4gaU_U^qy3mG&JzgP_;p
zj@79uDxDNM0v#X&``E;~8*(r{R$z2P4kW#NYk^`C$PMZ5nQhMOA2#Yia_GXE(#hPN
z+wsL+`p8FRi8n?BD9_l9^omNwVv=+5wG_)WDIu9gh15>td?GaOJ?p6}(zy59h~<W0
zS`Lzkj?Vflon}_k7Yk>DVO!vR&ug<H_}NO#tgeeJz@?}vjZkkf?>)V`p50|nPtt6u
zz$H#eQUk>`%LDcRa$hs^VQ+kl%9}APOrvnhxDBtH{x;Y<14FrUJ%8WzH<oI%8m*~s
z_A}+JOG~c5h)QBvvtgk;&fddDEEsI$Nx8py=-Vc7aR=I@NLk^(v}lRv15W7L$H7Wv
ztl=I&^rtNyo&iQlr4JZzC&k<*L4yx*obK`{&Quz~fKF+rl=h_eX0XAS!oZtyTfaDv
zFm7l*)rxYT`X-rp>1#kxH}@>N2YR^5zHlusHE3k#e#nAJrJ#xb*}Z>5UdK{ulYT=R
z*t$e`tywx>-%w`_9{lyIQq&M#HV^x4^9iGDljxmQ)#PxWUMWXsv45&8@kt_&=O9)S
z)|tGi)!Q#0jQbvQZS}xmpGC66JZS@(i(mfiht0Dj3X0<$!3Ds~CxE6@2ig*7i5CeH
zyOG%uC5H?gxtUqKk#+{nzepY8`Q#Uq|5S?2;>oC2Vj-d+v*&(P|0%(_HpmCYi+<B|
z|Fldce^J&h|Ddg7;bnXlUDJi$C^XWmGw<)2X7y!)yo|sHDAP|h_%s(D3IB1CDc0+s
zHz{FuCyV@kmBVaC>>wnNCXRMe+QDL>eAy@`XO8OPlgu!EHG34~gq%5Upy{MXz3V?3
z(;bY)n<V3idUglQqDg<XQq$?hHk<#ce<g-Z*LzPzasmGoGzInbgNL_p-0bHsL|#IG
z*?(hR3wxOXwXfwuD?HlStNZVQhWyvQC_XvbO6({X;&xL726j$f>VLj_bfC}XLdiUN
z+gJQs49}&-zhs`_dve#Fr<BG!iHk({4Fue0N&dh#QQ=>w1p*(s02B;b=^FY2o(s$%
zgot>A%3L9>q?}Ax8Hj{};Y&l@hG5r1uhKy14s~M!w(>`ge*Dd@EGY2ld>7Z*v&6n3
z{7O2e4AUc>*R!fNm^{|`4B;YMO*;c;k0h2YP5(Y=MP-3QLqM@~Ok$k+za66&$S!6%
zC_djRFTMW7e_{-r*gDVk>=<R5k`&(&Dz~Ht<M{B8JN-Ug4OU(Axmq*2PF@WGK{#jS
zN(FM1gc_yt`#{1#>SgD_BdZO;`~q)P-6ZX<XYR@*JG;~zzrw?##B98MC{ab14W~1@
zN_81FYommEH2RU9z(>?@q_KBxkaPY-@O$O{IaGx+S3hP_-KLU?=axLwV|D)iNaex}
zRlTL{fAIxqIRkSs^hIJz5NR7@CvJ_K@uj0WI*tJwZU}X)RNyd*WTvwqs|tvKb-+%J
z;OM}nOq}GE*3revqU|JFm%s4H>}ohd#tZzVwfY=~nbdeioV-0%fca8?fMZ<{Wm8~t
z|1XD#LdK9hSzMQ<l(|w)1LjpfACIPVM6c0oVI8%4AR7`GJ)|yK*LFC#OZG^bRIc$*
zoe<z49&-HM^hL6}*G*>FjO$!%AK_meJEg~64Z@_lE(5PLw_J^ObpcDbo=Q+XFc4K=
z@DbJRrcq|V+|jWzMR4gt@J}amLV$W_TJ|y1A&12<)MGbDHR?t6ZU%uqfZZ`gEq>5e
z`i1>^T_lfqG6v*ME@((cYB@lnb@<xiWa>|%#}-BqqW6c6D549Lr)_EYsDECydxf!@
z!oQ=E;a44IABsl?2^AFozk93^ilC*vDPxeo?o(mBsz>o!>0IqUQ}uu%HfV@@Q=Yz~
z(Ymlx6NQ7_yd`9?km3b?vx)|Ra-E=2A4Wk=^bw+Rt<<cYj$udeU5$u|WWx-^nnOt-
zGJAmhd#RWI_)&W&!>_)|2+|-m+EA%Sl*S0S&iKoHfNm!F1+=bv?Lg196Ifp2tZ%g*
zGJ40LMCYML=PaGUbSLLmk$#`N9!WScHcvpA)H2%~Pd5u298~Z*<*E2@qwvr){t{F7
z0K3@M4qniVks_Cq)&le5wzs4bi@N8nAlS*`Zbf7JSlrxes#()colX(yetL4$%L+0(
zj5?}=%Z*^rLUxo4ggDm`CDP(Nv>qp)cPzxNl9332@^qBECM1{>x`gU|a^nl{7X-8X
z{3Erte+FNe;7M1z*r4tkwTZT9Wd@yt*)`dYu?GeNJ`_X~)9+s{RUun%XXGH%?Yn)Z
z9@1dC`Fg)BS&sSsSuQBUQ}Y5u8aHp{YzCw_isF$XXOACEBRNhm2F%D;T%i054UGAh
zsKORU=YF%2O4X}*0}U+*n^;eqMx#v4txsd1Y~!@04jX9t+2WRRzTY!+eqg_LB6ski
zE%L{b@<2vhQ3YgiZ5w8yslNpD&csR;*C?CV5?zm}2O*x7z}!RSjBM_@A$G(^+0`9G
ziNVvw@t_OR=>fylg`)Wj-01^Y!Yj02e|B!~s8gqOYeYRXczI+oe#Tm@@;p)XTdoDa
z?9UWS?L6W4$NXJUNL&3x$um$}ceTpcDXA;*hm-?3*pbm%mFvj?`<<7TZGC&;Th|rx
zuPUZKByCHws=12q&9!6Q8Nf{~w~Y1vO)bWKUuv8>%#f(v^F@t!4^9S#uIqGP7^)4z
z5eF5inX{YEjt)+bnY{-lAf3diVrqKmGZKB?!RPbDt+#Cq!W?(+n;varAd~ejWM6gE
zuBEV`M9sT>$2S2;cGaU+@8VuZXL4NdarxgV-Kv97uJgYJwBzN2&x+7y(M>X0eXh1{
z2zFrStYi1|etBDh)b15lec7uM0$_go@W)o95^DgG@M61JyZ^%55VoikpufIpgP{35
z)vQ~;fIce<10qHW9`;=^@8k?0uK8W%sW&&w#P-xxn0;h`&28gM47X-_7hT{4Dhy7W
zzCsw_-jPa~it(s0=^YB`q$sKMWV@p>gL6+&3g&a%zJc=X$x*}<7;slU(Zn&}0(%&G
zb0<zA?!7+QG9XS()5FJB#Pq4LrOZG}&_Qji;n`luCKRG~0B7q|y;dtfrLJfC1wR`D
z?T+4n9jph4)#GSXLcOs+YQJ&$wRi5f*QdpX`DZ9^+kw34P7z_!soczYds%o?8(HQJ
zEW7WsnR}Z8_^K2v-p-QbNlMa9z49BD8*GrI(?@{Db6u<}fX@ayJTZT1UK(V(Jzc<H
z?;4TL-awYzP$_AFJ!B<CZ@SR2T194NalKj9>lyBHX6zd1Km?ojEGBc)gCU1^Y-wFU
z*#ORjR(&=+0GonOxqvnk4BLGppr2IUri0YgCxKMD9g!z18hDDc4@=ovW%oxst-uc~
znDJz|d*)vGjz~U9_wNw!i?adoK5LQLAFS-nedPjJ%dE!WdeJIPCQGR4+~-o~3PBYe
zz*S>Ru;^)l8-UzXA$##|-z`^u*x-;y{8`oXMoj|Qjahv}ek@gl;=e}9@<Bs=vJ<<6
z$TBTp_h2u<fcG#kn>;0*;o5f!@lGBlXT$M?ORLGyOOPR$p0I@}%G&Pc=HO*ua+FKE
zIh0JLmCv(4mD9A_cI%4O9+M8n1cxNQq<gjPx?%?ce3!adwS&sybnu(rQ}$A?owFjG
zp@NF&jSiX~nx0!qB;@j=+=i};X}UAJGHxC5b8qFJ=?a_R*ozO1HC{XoYT5t`ErXv(
z-|2NPsQ~I6Ktt3a%O4_WRqSC+Nx4Gnw~kVv_Tp&FfMYOqH`5`irYyLHdul-ST#m=i
zd>d;GXcOXMph-6|o@8}>dh;;2cGajWXG-y*ZO!rV3w3{j2oWp{GmVF5z%6!j95<~6
ziGDo*`&kTNt+Q_E5G~f)h;MM*AInjs1mYbB%2p`iMM)O5eNWqYul;pcjOrb^y}Qq2
z7xFjMc$sdCkSfA1iV#-h+@}xX1BTr#_PKM~JJlBTUvB$bj!=7VC3WJ;P&9LxDD5&C
z+IZGQ4_shYqfh-;PIw%ji`<jyjlC7s8aKJ|jRm+l(5k@r(FeWNR!H_)z}KlWYR@kH
zQhs10Sp+am<-It=(&t2o=Q7Yv9VRx}vRsFfW(|mg2<@7MyAAZ6a>!po^NKn$xSlCa
z`vA6i_S#O%_Gzj3eN;z(Z|RoQ4Y<LTTxuVFq6&G{=Ju6v)`DQ>$fNSgJ`1o^#5sFc
zY}$=l=d>%8%MKTJxEzBwG1USbGs_lFsKQ2S#5N(#rmTNwQAIgSjH!$DTLt%wwcDB}
zg(XP8+P!;+QZiLdI6;*?&4vMyjZFw`*TyevMEUFBfJmE@GApZk7ce$)J-1H}47vqJ
z;5z@2tyd*Py7s$}I$cUK8RSPZq`euiO!cyyw|5H;jiRF}rPBe9JkOC8jO@!SDwBWe
z;>4LOK%mFLOa~@wPuQwMHj>pXU%5GKm68NMc$x4w#Kk`lolmKPde`<1Jx$#vODP;F
zWz8P!n$S^H9g-mQAG^yA;<X`+jAh$U%5*WVLEu7@wSkTjg%j6IoSddik}y<T;}N%h
zIhIwHZD9?LtUSf^_9t6Jut0Dy=I=i~vrdaZIpdkJ40|R8c7;xxu}yNJnaB&>(+ll@
zLg9b)GooKgXN>`~H9=qI!LvcjhDqDoCunZzc8Up`_J0cfMfc;D6_aA`OED4tDKWY&
zW#}_)v^f!^h+WLVFn33HuDpt1`PTuk720LV>-AEs1?LmPzTPclz=a8w&Q8=&&nt`G
zH{jVil!?y>82m~g@~3mSc|!2a@Q#mXAY|zv6i<I~8P}x{qq!M&mtiA8@_WKbdCZM;
zuWI;G9~{$n*)=``xzlHVZ|gX{UhhT021*h<8iZ6#t`bH9m@DT2a^p9SQ=?f(K$Do@
zZPt*#6&WTkrZ%!P-I;pQ9r%;ty340hsz)|66ZtOXe?h_U^I?u8Jcs%5kxFnK&M*_0
zf~PtkR6~1B3s*gy4yu5FU(Ju^;cu`WUh?;7+GadvvfMS?@Sox+jldH4dOv(b7F>zM
z+<<Pi!$<>)n&xen1)fO}e$DU^wPZyVv}qVmlbx345ZL*dEs@2Kkuz&SxHy^Ev6Y_G
zTo;|2w<tyLqLs6K`!I*DK+(3#L#ng1$1l{{-NDY;gt5M=nArj$cU<Xc9=5akMIX!h
zO!tcpwtQGdHtH3acjH$Ib9SB*7wqR*rKh5&zIwIEn~B0wA3b*=Xxgx7-Y!dG{>Sm(
z>_Q<qf1<4#m0;Y&+>++5ullnH<M)rfU7OpkHJt~5sdk9*Ovyu?ez}#r*0s;qdQNl>
z%oF-fEv(hiDlq(+y)v#kohH)z^bX;Wt0?+&-^z1j3Q~=H*D7revoAod(_Kbh<6E-C
zFFBhd!`IhCD^&^L(HvHfFP^r1FHlb)Z=DqRly?D}oTWEBemSx<UdY$ht3Heq^JAEN
znHQwL6#nz@I!XLpPEXqd?(;)Bn+|Qh7ZxjR?V81L;}KZD`OdJw@9nU~hS=}0zq9Zd
z@Ov$N#596MHHQM~Z4f8Dq71Xe@y(UasL=YhD083sX-gz)QBi+rc6axSUM%E+tsue$
zGRi{5^n10DtJ+Zc=b7;f5$4ievV#r(GFeu8iAu)TytL`-a(2j}YB_CKe?rJ~-AbCy
zQ>F>i{%hcLVl*IFc}F<W9q|>WkNg4hf<+^)^glYMHWm=TF$G9l?ROezJv0ylWxt(Z
zqr(Do?aSk1WhG+J&BMRKIp$4$nsL&v0_d5&<bI%;)#-d!LtPjn_+YxeYOt-8Mr|V7
zaA2&x{;DsZJ~s-z<U&6Bj^e4dOmi^DtJbU0QxV6q(J(kX0BiEyhkyD><l(DVC8&s~
znfgSV)G^h5WG6y-g@PdxD%pRNt}x4R*#1FwI3{zq@~(I4NsYRiP^h6WZ*X&JIXnC$
zsy#<@K!k??!yGO<wrhmOjCZO1XdZr$Y$wBTCa}8N7%3QxBdpvvGH)*A(t<E?uCDo_
z_h~BfYXeaKMhY}*R)peW>zoe?I^~8@7yZbU`TbcTEw7)s<83^0|I&!-lkh0GJ*TRy
zgj>Je{(T<4+_d&O<G|IBWjSB?et_Ab)+l7vfVLVK#q=qi&padFP-jAGNhpTb;EPIc
zH5l5mrlR|TVf*$*_i%X*J!i7ji^-o-THAk39@5r0iZzq<{^ulKAJLT3wG+_spYUX~
z8mCaI5r@bsbHQ%?j-<~%BuHrZOL}CMH1`&dHW%xtTCle(xR>|4n8BMY_52b<=l1Jz
zzFdJ@Wyet+u})zS2EQVfLc#yRH(%EA118lP)W{3FX&Gq($4Kyt<R}&q;-aa(t;Z^W
zP|*(#^i7eohv<VNwMatF4Dzccq|&%R(xRZIYViHJ>LChhhYZwib!%v!npdsoty=wy
z<KAHD|Eq+=R2>@oN4u~v3RVdR`cbwv>k9WKtTQ<5W>Vd0v1;*lkNlPKbArgwk=BnS
z<?(`{BwHfbDY0#@qEWwZ5x4VQ1!F(DAnLW-yo=}2DyrdE&YmGcnZok$UKP(bZbnO;
z^Wj&C)_Q;y|EokWhR8`)AY_GpY@1y7fli&qA;Yih<PR6T>=cyD5I3UO2%KIi1-9`n
zV8SQ0ogQlD%9JtxX)kJjo=CdU#^=;JN{j|Os11_DmQ#!AfP87wcA)Z)Or)dk+d>Vn
z@o_<Q-Md`k{{bsY2bi~x>@N`AevoS$iASB(OZYO5%m@PT5gXHJ`HO-5Hnz!*&+KZ)
zDx*b;syAttgg$Ha^bEFT^ZU5YiVURU6HwojlZHO*r`eVaRV+3?Am&V})PJWGZMCR1
z!aC0tWDNC3nL>Vn>Mbvw<!q)?>@wRwu@<z4&|n3Je${=esgcu?u(AhZ#KWmPrZ0xO
zRC|{&VjdmTR588jZAyfL_YDgQCPG~QGf1g7W4>jn|3n*A#T|$QP;~#ru|9KBdYlLA
zb1C+5x#k2O6EZjMRjn#lD<-K9va$+|6m<fYPYY72B`2df!M^EoiYmeh?=M=I{y_jS
zA}nCFgSJ3U)+!ED%vVIrQ4rM(lVd*wL@o^Nuau2`L$qZdtZJ@=%@nc%y#O?ZGy%U-
zGJdlvZQw4=riW+vO*{?OS95N@Empv2YmU?iQG6feabqo)H$tY}?*E#osDF!C#5Aga
z1YvU&`s`KIn~mG#^h)+O{O99IXDzX%kYF>W{&1_k;z~zIo;bH=zwmr<8%00mhW;j}
zx}3=zd}$Mn<IZ-EW%ikkB^bxUxZ1FuH}q4J!AXGC0RBJz$2$)X#x{pp0zM!-Ie<Lf
zCC9|77!7TqW)$QFR2GtVxk3(Zhp`ELMRwRooj9rnMYkCpH_{Scr>^!fevxH1NlY(z
z{hpT*T}e-KO^N<8w71_2Fi?p`IlcVn_(A!yEX59W;hH!HJZ4kN*P@;Bt>F1<mDtF8
zF}PzG0(UG)>yQ}1pExAOyJ~+LB7`QN@~Nf{KWkzzd*`>`CJ*5Q=xZ1@H`Pa+X{GX*
zjeJzK26BJK75|0K5^6h8j~Q7~so7@pMyWjL{gPwu7Ew_gtXHdF#ryZ$C14U5hMc&F
z^{@GX@})a_uP68WTH6;Wz{RlfWr!b~iC-oM!OK|`_X~8!tT@a%^fDUnVdUIR_e3tT
zo_tIa&~NpVj4+VM+$Muz%Hp+g+*h95KIQA*0M1gG0#)&XjbwsxrQ}s-6>NNDvI@oe
zCS{krRtu9btu~jqyS)UZJb@xW8`cMJE$(}kYCy%K7jdIJ$_gFLSj%X~aV~CmY+HaV
z{E!3#oq=FkRmW{c3#W;yG|O5d+Ic1Vq?QI<(IX1GoKys8RQ;uY<moUJ?B$!3|J)bB
zs8U$Ort>Zp6mN#*L0-2dT9LLCOVgpgTaVKWTvtd^m86c!{f2@LF0BRe_TV6+1pmI|
zHA4=fl~^e8eqoP=&`0@abb!)ZHKMY+<Qr}yZX0nIB7%zY%ZL`e2liI~(?3zvQsUB`
zNJ$Kida^-o)rNdcmcS=q<W*PG`e3}nyCdOZ(y=<9ewh%QZgd!n?|O66e$n~Se<?ZS
zwFo8}zUoM!W%PqZ1i<nVOzkNFTS2U9rFQx8Bf~&s*ElkNM+Np}cryKg9-Ho-kqhJ^
z|IuWR>UjZo5J{`0Ma5Cwx8tP8q!K=9$|~Pk#Zq|^vgks~S!lwZkzf#B>>iY)``VZ^
z7U51qukOE36D<(V_TwUc><M_F8QbWj4N1OS_Lp7lOUd)?Lh+k9LHtkRHuaAp%QGz?
zATsU4?v-KN!1tqRkb5nT@q)X`r)o2s0A=R|tRI?53$v^NlnMekM*xquD(X?;84mdb
zG&!jqjRm1L6A8%%g-Ri={!Am^J9+fF<bZ{A&(Gr)KkUXm6%HijtcfygW1`526{H&y
zd)D-a%Wi@nwA;XSCYwp`Q^$yuLT{s19@WwB#Pvn4q%6cqE!dEw{E$DIeLeTaT{K62
zTKetaI;}Q4_0&b+It2ygJvZSa3G1=AvZ(Gt8Ka0>wtAFAY)k06V>bpIfY-ibRiWb;
z<V;1Po5p*G(D~-C8LIAXy1C+!6HnK1JU3<5gsJQ!X6gi|5?BDKp(%+jC`>z^hJ(01
zmG<_FARcuDRif;VeO^IVXI$<H+W%>EzSartOU|BoR&UyG!VF=S<Pd!0%V?Har0Njm
zE@5<DSTdR{9j)9iMojZcxI96d0`;z#oU;Q#>~+c$28A4)vO}?1SB0o}=^i2YSjfmq
z<^9SBpnBIxg=B^tO!w8>%=j}1f32gJ%tw+5XUu?$0L1Y5st$R7|L|`{)>MKLO9^?R
zs__71b9rJ16xdhOr?d<a4!7{B<x9BViX}#R6d~F53me-YF&+YZ^o^t>h9{A4oa*q>
zr(?oB*6!W6KWS3Y!ItG3?apMaKK&_GOr4^CKpZ?I-*e;yWe{ij(W^lzu#XOmlbv+0
zZ4mLmJ$!gJ?hgBxh9mTYtsMD1|2a?)-7W$D#?<erD$aQ<MfMXqXzA4x`PbwPuG`zz
zm^sc`2m~8Eo#JMu2PSJ<mUvWfBt)n>O8Fztj9Yfi$JqAD|F8$$4)+oO3I`b`FE39D
zfHE@>&;kfR^t|;9NC>gfNmkGR*Q=U1D{d!N;tWw!Ms-5rrbeD)y7IgYMXiTy#I9E8
F+)D9}`UU_1

literal 0
HcmV?d00001

diff --git a/public/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/small-s4-code-0.avif b/public/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/small-s4-code-0.avif
new file mode 100644
index 0000000000000000000000000000000000000000..0221680ba535d9f1a3e3d19f2c7bfc30ee3dbdfb
GIT binary patch
literal 10564
zcmYLPQ*<UuvyE-rww;MFv28n<*tTtJl6<l4WMbR4?c6!%UuWHV=-Rb+VRb$9Qv(43
z;h8ymI2gKGngRVaTT3(Mf3U5k;olh8!PeB-@E`uK5nGtpIQ|a;0XbM2JO79O^K6zj
z_QwANqJlsmK*0YX;N<^#ARtQXzuaG=`u>jr68jr+Sz6ltJNRD}{cm9UZ`$8#Lsur|
ze=7c;{Hy#&A~;w&+y0~e+pWgZ!O`I_j<j?zw*Twl3|*N;0>I$@(a|iO98CWKKtP~S
zKtLdJe*uz%rMu<-7^uJ8fBAU-P=7oBPXqo(;WBh_7x{;FakS&Hbucym_YmeWwlsF&
zaq@6-Hnnx)`I}>DV`=E<!DDFSVDaz1@GTu||6APOU4RCFfC7Pl1%rTx{_TZh>1g`D
zOaC4Bx7g<2b^X0EWvi`;p)(2)3=B4fg9m#d2o5|H%bJ`FpS!!6()8qS8H_P<`KKb^
z`$nR%tODwc?a94m!WD=GInn#l*))T94nM<O?A2nT!=f&8Om5KBP|puZFp_Rwmkd~Z
zoevS+4d_pK)QPs3m+22~pZ?dzG;w*0rC{dsRA(P=EU2`~BXGb}>>vIIIg*+=Ho92!
zd#iaikn&u<BmGx!?uG)QM@3S=i*FkGaf(WxHmPPyUBItid6u6;wk^6^r84{zxetb}
z1I}}hDuo(sLM>+`5_Yqx_Je+q1*^K}a2e^N!wGTz_N4CDW!ibBed~6Y(ECG?4q~C!
z8zExl7=X#BuS?Z|5Lv0k?xtqvkUR#NKWpI%oBZJFc_~YKc@B;;(HaC>$ZDP=&{ql=
zh5J%#uMR0FHj+2th_4|Wnf?Z(W(pyc3PI`_-S*}{lsE%)m|TuFYw3gS7UmOlzRe9V
zQG!;8*s=RjgXZqO{t=WGXFMsQN6Qq;(-5Kt@pNi631tvcglXCn?6jP0RnQgV7D7#T
zV<i_-Z%db}XnXK`o8)~O0l)d4kx%dP(0e`1Q#W&$kElc@dzdA|?{F=67vEO3Yh!pi
z+OIw$_EyJq`ok$0J}(kLW@{TDYPsEw4}D#8oi(#ssUy!|cYbxpOy!MGF!?-uV+QOP
zd8CuT!^Pu$#PVcX*TXTbn(2R8IhBGpHe|uM%Gw+y57c5am$#V&!W~2yvf|qn_AM`N
zv*0$v@b}qByiXV3dWo7jU)}@|3asP9`j{&OgwrT$CI1d@_8w}eRuQv@5LKHg$K|$Q
z=o49M;^#?JI1HDcIf)s3MoeJ8zL^T$i4_yI{{tJG4hPut{dSM_XgyUfp4m(&g}Y<|
z93s&632Fho+JB_iNQxK@yJ6V8++6_5H4+rP04}F7v{Ue5w;j(|4TU_rNKSAgN?*wR
z4hpwAI}%b}E~2>k8}l9|og}Uy>jy3mo!neZU`b7y8+ywH4*pOiGi?&CCw8<;Jh{bw
zo34Yd#3x8b*e`_%HiNX8Ul}fGNv*K1VeGLBi)~4DCgIeFSd?#RscLHSHOJ&zUt4g3
zQYv-l!(;n>h~egbVM6vw!ZnAoAlKw89K%B#!UyKJ%MU{J5%Z68{!%GLB7Bc>Zow7!
z1^##WCwXe9#TI?z0fGLHu4s%1mFhQBRaK3M(M>}yiA2%pkYzDxg3Ju(^!^hsw4$&;
zJ$(3FLsl72?3e+&t0r7E5b^nnt{4l3H^Ye@CVvpy%S&M^)mH3o3x$E5mwyl_DYsv*
z{`e}|h^ITJeyrPX-yukGrB&#7o)2xWLi=U7`i$5#au8eK6PJ>$f>RWo!u~*HewI?5
zSDjBx!w6|Pa=px?S%{U6z1(e^Wiu+Fl!?y9OUfyGq#c>WQ?b6r_H9K!r>B?gku25h
zR%g%5qfX|Pv6wXyI%ATEX#<qRQIt`GxvJC?G`Ut;N}x*e!<V9|9GiHJ1H=yIDFOX$
z#IZCJiLo$!DE{iXrI+{ccsroCbBU}WNsv01KM*4dFNebBP_>f#7_@Xei(LxnwQ|VQ
zsaylA(&THHaL9`QL6pB(LT;gJco-yOfU_<na`Rv}H$^}TfT-NQW&0pr5ku(*<r!iZ
zewCsD)MJ-a_bZ;v<ui=wsn#ULWCqeAu2CX{U9IN<qF{AGry3Tb{*Q+b1ol%q*z$5F
zyu6+ifzuFQ@VD`q12qx{=|bqZ<p;NoQW}m%AXl!P!#8pn%g@2ekCg)<MGY4G)9QeL
z+njrBO2GWuW}qanX3E~DnM7g0wC#eH#CjviHqMS^p#PR1!||~>tp3Cpg>m;_Kz>Z&
z(kFK6lo-L9i*tVGLzV7Y)!x7@4oMX1F9!?rbU7xC0F}1g)3@)KKHGb)ID&fQj%Z_$
zFw?0sr04RN5EY{tRh4oZ&jpfiJ88H0=P|*89xgvNs!laJvM!aW14Lh6ycgYj$>rwK
zoy^+=EgR6w%wyY2Ekq{#{6S9|7)F+zNqE!w6DIlt^ES-dKt(p^ZWFuW3{}Z=ZI?!6
za=8Pg8_<Y)Pfe~X)u?a>WQ{pBAy+(jGo7~FK?%wU7<`542JbHrm9_}KYwm(yeu%hK
zZZCnUcX`TtLg-C=7d##lj%V=qcMMs>dghOCQ*n|fBU11(d``__U%-t)JA4Kg1nLcz
zQNcm@$)^p_9(KZlyTvTnVao@u`o7=6;9G-A#*fY2)f2EC@C>Z(9QSaDJSSg6$G9OZ
z2!+Z%P8=GzbG?dc8AX9hxEHY<FO--I_iI?ie3`MSSfV8Ro54F``={0;%Y#l=9dX3B
zvF6jatxF7Q1KHl&O2S2~I?Z5|@r_2oij2Ova%h^U0%|e_-zY<pB#MT~UP~P~w3UZ^
zunUPVQ_AA8lJhkj1oTF=>(9~r!*Z6V<9Kg;*CH$4yF<`Bkvw2Jcv+v(Qk-`joP8S_
zRzd8+Dw`TrICG*Z_8DlX5_|Lv_FfJ<Lt8vNCIQD5bX^qAf<Rzb(Dy#GjHCi4Wfj%h
zvVLpt2z<sA1p=165p?&hJgqICtCz+Q*w9j;XqsBHGKBYjz8@`w_+2M@p8l&7mKKa&
zTIM?*-}<dZflZpfmuG@b&BYeCU(&|%>Swto+u)_y*Zf98kW5JDyO{XqrM9-YE+?^d
zG_qI1=w8)+%mMO2LFv)B1P|9ro=No7W2UkQ%xmlCHY&RKp3oy&aPtbIqW{Q>14}}7
z-#(wXRCc(0u0>su?WBfsXS5Wxag4ntT16tq2Mrn*TD=>vy;mzB(tQg9ej}uKy;2D1
z=66^btY~t!0dl*P07q*D3ZR@&lZZNs;`Ff25S4@>!{;o3!=L2lvg4iikX7{HV!z)S
zaSfLT+Jvk9!-&EsFxPh}y~$611zE=^K3NL2flSIx?-<nCQg3PicgO#<?OCFF!7!}3
z_&Ji{a<q!~1aas)QkGzF3w-i1)iU7%y%-oFVelE5_TBm-LNM`hn{4!lMejE`5U#-c
zWhCwh7OvBb3e6p12m$(zqR-rrlaMrH*3=~B@YDPUWTa?inid>(JS~l~B7-sZ+hTmT
zMui=4gRzXY@N1O|(p|+H1@!rI95FM`)eC@5sv#fa)dieEqEFoqT$TFh)wKmi-tx?G
zihCRx!mRL}FH#zRdd9K4MFx)knC*w8g2|`u1`EDy(-D&=zrct~sa>Afu(IU#9?~H1
ziRA=78rRK<A?bFfNB5Pw({CAR=R&|#2wXI7difkFohKMz$kvFRG*}w56-M`TagNzu
z5eXvP7QK0YSb&>?AW4G=U0EAHP*^x5t*UBK%Tr2X;HU(q$|LszU%Y$DHYeN*>)HxC
zEai9=`dWLWevcI*rjJ?Yg;XX1A&ltkZafQf?R-5isqTx84FM&-+TU!&?0I!5Osyuk
zcuTltU+h#xMW4kQwaPTyAYFl@+O;`I;t#cFqE<uwmrv^E%VO7HCMsgHe|DLI2Uu=@
z>oUX`@KnNUF_I{(Rl0h|9%sxY^6O8mT3q4x#YSN!8!|hl`whX-8K(HcjkVk}BlE4-
zlEY&~O0->H9*#jlZe<r+(cTiT1MuFYJ0_iW7rM<&Gor^SaCS*7mx6bshJrq$VG1m{
zEcs-M+7pf6>u~SADh+1zS~E9;=lg}y<y5*_#*b?UIpq6AW$i774ul(-4&7)9^LIxE
z3Od_}-Xs)wKr#3qZeREoa>09u?2kodCA(bh-*gq=*`keEU%?`8)ri5mtQ}glel4qw
zKjicK^+sYh#aC{xph}RrtIN%uTnZ1s!oIQAksm#UK#RY-P?Cwu49}=cwi-rJ12NfQ
zkB2|R0KDyJj`bLq;@!1Rlq<jxNcmMmdN&-xZxGms2rkd5ZW^JS!67$Vi>iS2fD~x1
zhBN6~@_rGVIw1YiXf!WVFWtecwMLKn3Jcg|S3-8VUZJN1E43iY%Tv!5qou-l6mmX@
zfSL;~A#397z1seU%8b11Hg%#m#UKn$)<KH_6W}*n3@~)W$r@n3&D{dN)?zfA=;V^l
zS$Q@jF#p=KZ+tARNlrhkRt$39kCft2wbJi-*w?cw72W7qhn!{H{TlIo(J<%HS2>WY
zPWB+4JSFYJbshM92PNOFKv%*uD-`R{g-M?Q=P`?v9W^Wh<4Uz{$RFm9^JrI*jC``j
zl<3DxsPob%DbvzZ;7%8vS7ul-{+Y_wRPxHBVkME2jYnzx*e6;--%wGgEsK1M(Rrix
zV>DToX?0UZ;~0UtyHcd<&CwACic^FhiL2FvBtK`}vucSN(yPo0PV|%q>^gR70y*bT
z|M=IWx$muey`|?mWbO`YR)ATt<2?CZqP~H(%%$VP>B+VCg3kmqye13lOQgc}ENN5&
zmZd=l>9a#h3?gxEKtij^vS5|@Gu(YwuTD8>GO}DsB#&p@i*kt_KQh!PLf6AKg=+DX
zz7EF@{Y<tF@N^ye(u1)*+hWsv%znlnj3Ju*8hY+`jQd)q5N<B9=nCumO=YLX;KjZ5
zsTrh`SVz=`>L>Gdkj#Yz>!OnwT>XewDGuJ%F8k@p{d32Uu-&cGs-i4qkaMxvpe3uR
zM$3Q+E3bJ<EYfM+^SqRuSYxxM^t96!u^C>!(maN3>#)(zx)SI<4X0jc80S*TKWa#b
zbqddWR$Dl9dx#@4UY2Z{+Grfb!Gz4l2g9r(e+AGnu_LpNN_Pz(s07}<>a1WOKN|SM
zSu>%A3@-be@cr%8`6S*=VoaCt>hHVQXq%O}tieQ6UG^0NJRqNS9=MKaE=P8-OVg$c
zH)V=kHU=Wy;DvvG2I9*D*MbJLb-p~U2%YJ1HzphidmPHkHAzGc2c{HcU(-5O3#R38
z8|$4d-bA1tBylFrJ?m&1o`Y=*KgeEIV<#+*@^N&@-`j2`(k|it+|tUYERL0<aEvdk
z0Oa0^doihA%D4TCDU%~L7dYh(Nn(A-G?0UdH%f#ZVV=?H&N4tmz~`=*4g;@om$6TF
zsd{Jx^M#Zu0jkoviAeyp9IrdE=_(OO;eu?+M{U2$8zPPe>C3{j;}M>{n@55l<Z?*g
z>yTi1c+v<~#}{e(_a_TAt-<j%tfi>g^@j;BT9-?jpNii51-$RWGPP-T_>f)bnzi-g
z`Pd*>%MClDohrdOUuI{J7UU+*RmLh_WATp%Y*y;WP4AtB^W+oTC4aK(7%d+oLypYU
zuebLf(G2y;P%T}>E1u|YkFMeJl^(fad(on?zBdOo4iSVom;E`@6tdUnJFgO7F3oF@
zRV;EGEZ^4XcC0(yongiUN?dFiFyzf^sFW-;OVd)r(%b~Ucf1FG=sOo0=ALSJYBpY!
z(<xyT9rcsQV=N%DiVv(3WXk7-Q}6D@gOH|3qMd0{3bG4L><x?cga~bV=WM}ay@uQT
zh~4s#o3upRg6BX1!9R%AmgbOJtIcN0l>%dQ6`#g`;k0>|#Dxyxao2Hat{vnvKR|pc
zK|kwzI9{3UsG^f0!b&Qtse66eR;4I0Ao@6xxT}L}waJ?oGUAuBzjb{)F^+p%b~%B&
zgp^+$JbKE+9@B}p0G@_(kmE9cH8(L|R7q&-Tgrr10;`p}TI#1OidZ<3arEaMSwk;;
z?5ZgZvMv0gEv-@#!iK6jQQ+mWwL-Fyu~B2aDIDS($}!mv&yb+B@X(QMZMNBJ*bL%f
zx~EfW%8epGL<>P@t=1MjlF-q+Qonzb6vk5y5y0OF+Dfoz*-vU_XFxokEPxdyUb+~Q
zt5BayWW8;(8X;3gB>yuTQqM7}r=31fSm1Jo^aDO07`_cl$Uv9C1jIQ)#9G|<>}JBB
z9JBaBT2I+Kib;sSC=a7K-<xhOAfp#O-g<oYp(~CLy_vEJqd|M~enXja_+;ye`CXN7
zEnw1Ee1=gHaZtoVvol2*hR=Q3FifQihDTX^884);cYkp@T_8;^!5Tb<7AZ(Oyw<sf
zh<P6(CC45TSv2PxcmTS+Lj4=}IrL^Fp8;v21Q_ZNWzr9$NWWq<02a}FeGO5zV}MeV
zW&I#kkabx0U^yAY0uk3fic+m$$;}25^(q!39F|PvN4*>N&7Suqew1Pb=tF`<A;8n+
z=gmsL1Ez5{RR%D_#i9{)@wm1j9CW<m{xir<)|~$t@lU+qIB=lTAdtCnx?ad=D33YC
zF97E!KDOiq$j1eg>zD~-sXX&`=mhO!!juYTcA#0Mm4<+LwPseQp6I$;@0U@4qJ8NQ
zr^w5#4e1t@m*KFXF&wAZz3KSIV1#MJ!-PlW=rFYIR9-v<f(rYueS!FRlkPtwL9Hi%
z#L{{lru81+b1~!ZEKlY@4m_!z&gUL;a(#h><5P^0NXO!!^zZ^J@ocgtUq;qJZr<3(
zf#DZ}816-ucqkZUoY#T$<CYJQq*~>wss<EYgmLnx+lIb~!jchFkp7R+GF**V9IEV(
ziC>^6%pAt`-Aznn-!JZOg!?WZRR|le2Xt{rZcEwiL2mGD$&3&fOB6o38A6e>n^Vj+
z0I5!`9X+KOu?B&x4an*D4Db!91@9w$yTLf-4EO_r;mlYb;PNH5q=T~8i@cd2YG`of
zrvj3hDEozu&1(d*v&`yaJ5sRE$OCTyB3XJ$<!oIqk@Sep;Xp~^IJ#7Kx_QYFlSYQo
zz#(GairiZLOi+;_2ugXtsS`)+pclE7mbDH2+Cp;Qy+T72yu})=APdwuC~+^5aaUHF
zbco5YVvO)T+ZO}%xLtmt3RRLsg9J;Fp0m-65{OC$`x_MvhWxPu*P7E$+O`4S?2|^@
zm7%_w$Jo9S`&&WQBQ-orUeV4h`d8g%J+#eA?J2P@g{{8G$xQARA!=PYK{QDx;O7vr
zuuu2c8LC63DI3{ZXpv~s+NuKV!(h-h<U){G6Fg$w+?#Heh|+L2AWPdGT5Fg#$(Z*`
zs4^XY)o{-|PUwL_Wb|&xCuKKNDh7M46`m%lbw-6jW40>f9l>x>FRKOV<;_5T4UyP<
zjNDya-9elD$urPKrp(DsGQdOTx_ltZZT{A(N2`NH)Ac4YUXX7iGcNIcfAIb+OzSs&
z^&OAY^=tqU(Roal%%Og?Fx7Qgo440^z}{C}1^m-~a%6~R^^TF?v?gQpN^?Nnc0^Gd
zn0y0nZ+jNQl>3Qnnra~~b!Jh-*U=0Ys>o>4nbF`yB|aChP<K&qpWqgK_~tdEM!nG=
zQBb3(r7?al4f-*!TMerdvJ*C_b$5D4D8nzunKT`7gxpaRaK~lod+)2cYy7w76E`t=
zY$1>~6J4~&wp=BUiBvE4o6`dc*at1RS)SrROB1ePrpm_lOr!M|kiNKJq}_`;p+7Ov
zA6@~a4swlM;hi!b;(6snb|w2v5RfEP#NUdgO6V+k4+EY$Wd%b8s)p9={d2K6rd5Pr
zTLLu>TGCJ|>E31)Kp2n)$v#m(x-eaFd(p9*MAGTxVEOqH+WU4&eOq7SF#YRc+^2<s
zX=g;Vb}hB(HxRpm9>QZl8yA07(m)czMpDDrf@2Hi!@PLRDIl$+q9o6v@}e}HTjA^F
zGWP&)mR{u4p~m3BJj4L!&jW+uL`0ECyKHrGf`@i5s#zGz^hz$zx-)F3G`^<bLn7DS
zGB?Aow4SnP&BvMNK=+?m?6-T5BM^tZ=&;lOR3z~;SMQDc{@J3+fn265<`1%oK<Iy$
z^OC1>y@u6DV)3YD@~qE*mm<Iwl+rCvQ{gH~8C#_nhBx^O|GEj)j%#a7Bz<1)!h|jX
zE-mF|VVV04iG}&znxx`H0)FENk6`!8#zh7mwJqeq<?ey`bWX+HC3TZAiP0p~UORIZ
zO5!WdPoUu*+a>wJj>-gC-1g1k+~FeQJA=aU@`lYS+Pjs5^#m87pQM&1QJjME;%p0|
z84+&rs1zDXl6J_Io~}xHqeu-+_?gO_6j_~nGpdQ0`3X9sfji(BMc_gF!!kTb?VWyh
zr1QBoqMDA==eOfPmY+;*U>jH8lMU(ylBSzmdRxqIuf!A>-7_wDQbC(;xpXLD<VHcw
zuOAq^MlLmaY6pl#)U1uSDXeH@ofwt-v;}FOaktnKQPI|4Zv0iET+a%2pyiye>ajTD
z{R5<-d-D>F>b~Jx<pRHl!^a1iOhaSaG#pdyE8~8@)GFY3x7&VtXOi`v*MH}&B0V;%
z)LP0Uh@Q1_i;u349v(w>A=Bi$+Tp-36F8w@FRZI+O5W`dGF!R){O-Db(Yf|a_dXVF
zl;G=s;>$=^@?#lJA_e_XV)1+00r46NhggC$A*rJzB&=q-5-gaPc|Gww@w3HpTQk=Z
z_`21WPG$6z`;`X(pmAUC3@V#YGW}G9{Orw<_ZGX<xu+zPh)=|hyJ-%>SjtTG`nI$D
zQ#6>1usK@Md5;|E4Y?}S@+uQs*bVZV)$fofUn*)Whd4{V*we4XmO7{_2cbpdT2?56
zs<mfW_5?w=V#adeRXkB~W^g@DEVd{0X`LzsM0%)BMUpi}8ohcoXLg;0)@0aOC|Qf4
zKJYni<uWt*QA8cx_mvk*R7D@8M*)92shzN2GoPh}(h*u7`BldBtnX|$HZax3`|;$~
z<_A2Y{lt`@Wf6{VrTaL?=XKGl%A&x7cW0^wTSGT*mMJacdH(BM@O=;l2-2{orM3f2
z*y&EGU>5qgQ8X)9Omu50d{zTV`}b5yW;G26LpJD_VEc_AV{}UTLO<GLeDI)z8`;X?
zWb6=vcZ;)EC*V|FC0X^%u%Dd6?L(?V99^AXFg?m(*^oe9zv%5D{DDY4+ot984uaGr
z$F)bp|CM1M-TlacbDmG(1bB_9>8?Tp`n{S9;TP}|F#9}5^?5~@5e1TX6B6Qimv+G&
zoe3fwWSPQ^<Kb3+#|tyfP+@e4Yoxgnf)aM)vlEk;LUDpXHIK&nN*!s)Ws}=Nmt|j|
zm8)_aQ*d;5)XEenEoh7dcd&e_5Ck+KQSk}erA%V+RJ^h1{SC0{;*;!F!rVB40UL>A
z5sl45jld+oHkk%KV=*54X1@yfobe*RWA$JfP+7+qUaTw$ooIwb0Gndf^a&`Ul~Xix
zoF!od9(9FENr}P74YU-CuRKq0svBE)%z$%FrrG+dlYI#<>ozHFJo%PT9Li3e247*}
zh&nANe0udCf}`<W^&!d7&a)p-*6=qpH=tj(%PiA08b306%zv&*xz%HC#43WWd_pW_
z13HJ@p5bG6Ie7HblA?d%-pCV{M)dX<zDSuI=aHNzu6kIF==yv+kx}FKO(gKZv6|B^
zVN+&7r#<DnZgCZN<4+Yi*u|x)XUFZniznK|m3DG9P~?T8{&r{#XsSoRt|cud=3t99
z%U@-U!7kwgf}T46HF4HkFVN#vo-}dh`JO>0K(bB3>gS<K9z+A`u<FJ19--_N2o08g
zd_9eTty+lFMd}vRP_bb{MH=Wf9#vrhDP{2s^`Jm~|F?6iNstUp7XzE(tz)#VH>gM)
z*=v^(ZouV78YJ=nXJW|9A$3p)%pvMe8Z#1HYJG%wu1+Oh)ShB!SL=h&;;J27IXf9B
z`RMD0Zi@Gl*zF$t5hgygw`>x>;bp3up3{O%BbvS9ox|>lH`IaTsMB<%_ulv~+TDxv
zZtp$>$1DmDb783o_m25|{!4;iHba@G9o<ls;2TiP<v(wkpM?-Vf6{I*48QZDLw5+%
zq)xbH?FB*>*)1HdZ;ToFA8Has%|)D}o|r{&trWtq)aiqb<&wT379v#Ga-yg$gb>fs
zeSt;Qn3k_W7-oQhMz^1y8iGHrpJs?)gO(#yhtij8%S<lqDUK?6<`xe~G($N3z8zO@
zTeYa0-PRdj-c;O3b{9!A*hHBEn4rP+?i|k;&sF!h`c~l1S+M(HuvrV&R`?b9^^rLe
z)kZ$3M7!rBvDPVPg%)uHtzv}fUR}u$T<Dmb>g+O3@<TRcZs19(Kz#EQx5FqN5S_g`
zrI7d@!s&cX6s7+m7F|-QRwW=@U~EUa15>z$oJlMGCWRTi`Tao^FrH{ZFn0etp2^P+
z5yj1gs;D%%kJ!+-VmRPgw=8LvQgdY%5B>uOE_a`+-+ilHtg)b=TkpDzJp=|^UMDRb
z2hX3noeH|D1Cwu&96v)dsice%xmFA&rXntp@z+ee{r3ulFDO$ZF=He%{@dzH&7->N
z_YG$ZG7@{Fts2HJIR@#&P$1s7Z4T5U`!%B;NV)7ScH=KRMub|Kw2uj7v_Vb@gmklO
zHd5(#2&pv~5Q6-egvJCAe8=V;r@iBlQ2B0u9(9)d0hP~KtB(VJ)U<ah$GQV`ZZG{V
zosceuf;8D;_z5iYHrbxnf%DMr>M4Lt2fcbE-fQWgU)ztWh5p0jC7Su^NVGSzv)3@K
zJ1u0S7izCW<DhFnBbhS-gb5Xm*qN(e`S#P?DDb$aK9@H9nN5F2dvRVaM0c9F2e)@D
zF1EtKeMYh4g5fM^7$lFu1t7ZRQ({NERs4CHct~gvDLQtac%){3`l}-B5!s4rb#MT9
zYz`-4UX^D^zhiAn>>OsC_8-Z|0Ws_(G&9?Y5V7fuoDBX{Wvon-^z8!irHu11Niv&F
zUPOt9quuG<8(YEaB+NkPcv9E3b|XCetIxrFDO^b?*$H~<Cq{aHA)+AyZ}`za_3)vd
zYfpdFQTJ!}x3I~_x(z0`IS3(i0Za;uw4REeM+**;S}=Z3EqqX~qF^sd(`;xJT1<XD
zyarN#pYW+G^#dKd8r^5a>kinmFvdN02;gO)w8X#Yh>k*$r&dq)6=i5NB*i<qP@poS
z{DP?J+;dnNIM1U1Pil-tdhl;x^;@r08Lvucb{FnozV!_C=4IxlGuq`O9EBTq&h!mF
z!xe<-Cal<<lOUAwjJUdvzb!vnBKwJcFqTGNsjUy3WDO{Z7X9JjeVajkx{P)dA*C`^
zO4C%X+~vgc5FOWyttXh%LY)Hq(+VqPB_0)bnJv59`_HbIrD1qpHMoNrDC7*lCixCi
z4i;fkaN3xBZ)&3OgqyvG`V=wd)dtLh4x(c@c?a^ApRKq4J-HTPG{U`^&3yIMU1B3K
zp6r7J7aJ2-LPS|@-*G__U=wu7jv+tDUWh1^Yc%3RQf+OvJVvi(mklY1Nr@STur%jX
zPQfU62*kQMMg78uV&o<D{NmMx$uXSxYHhs6eD5FaGGSxfD9n3T`>YrRk{Gf_*Mi=!
z@Au6t9k5fOrw%iiU4M(Kylme*=u+OLe*tlTF06iGCM)_LmuE(xp9+5`hwhEWGnVS_
z-t3^fBp$3k%Wc6ena}5qLd{Bwsa2>mv<PRQ^BQ#=leBsbK>7ych_qzaJl<{XFP2|y
zpEET;7TMGGO*m`boQ_RRKJZ8+kUjk3cSN)V==b}zEVdYi5s<8#p+FMZZ0$a$*dTz~
zjj~m$McsCZQnP&Gka9$tv4Ro$Tzfu!0tkSHphF}MWFa~myl8uOR=OBoZu@e40wMyj
zwOCcB^@-lerO6$oni;iXTi&aCVs=L?^vnmq)*Z-?Fm7k~)&9g@f#@x!O0lDRv5_$2
z;Ipfw{KjwoEm^3O#2{1<n;9EIXOWH!IG|=y`QY`ywD4zv&9tv`3}on5^kDRheb8uJ
zly{f7g&j$OQZ`tgSak=nO@4{;RA#A$@Ma=TSV(y7YM8$5rZM4-UMN1eZs-KaxH<}c
z!&dtKWD(6|a~*%*w4!>@&__rbvX?Lu?CryK*)HX1d6KBMP=!(FOMSjF?RZcSK#*_x
z3Vj*s^AaOE9V6gA`pHCB8T=5dd4AiUfncM?B-V*bTcE7d_45cnQGY>}pE8r`W(h-_
z^ZE$;>?K$^GrHvuWu+MfT$inS`UZuU;P#XR_G>C<{m_Or;Woi*n4HSPIs1#+OZB;|
zeZv{{TPW8qK`k<4-TEd(6@$4!t*%^spcl;!R)y;irjvJU?%q#>5|U6(!n;qmsG@A#
zl6jmZNhOx=hsf(cV%U;LwVnz!vYvR-33LWL54H&+{Wf*{9D97&l_H#?F=OPQs%(kL
z`{4!*oRqw@P$x4SEvC5Jxp~+uJ4(Fg>*wWqwX-p2VLgWn40YKhRH212ocD0m9cys~
z!<Wvh0fh}bB2^S(Bk=j>blD3K+)f?m?SWQ-s|XcX(?_19YJJb^(xT(yua!zo7a8&>
zZV<Rr=Vn6G*<-_lxwK+E80><{q?rxjM<q-G#D=!d;)IJsJA*Y@qv$Nj^9}l`Vyzq4
zzuAhE%zY;a603Tr8sRdyn0JwQm%PfWyEJqLI{CDEgy=y&25dO&vIW>ledK4&pw*Hx
z?$S0p&LtsLts5PfXAq_eI<U^LuEde-Zg|xR3kCorRdG2rOjja>J*(J;^q3$u9xvm_
z9FhAGfmx3v>tf#=<Vc)liOJD|EZg94Xc}z9QW(2a332%&S%;aZdFlXC!RHCHnv5{u
zexd{$?-Ff}qMH}F%}G^fR5ENi=3n^w6inGUgZP~b`s468+o6>4M*K-_{^4SRUly8$
z#@qPozs`9ZGjK?p`RKIibNZbZFM5yx{o<Z_1O*J_FWRKI@<321SniarD>D2<NS0UZ
z<#$So_@6o%HCNR{&@*14ZJ~kCOa<s6U%Ah@u3gV=GL8XFaaeM0XmFAz3_gD%8S@O6
z-x+W-y~=iM0cm5f=Ju>4qDH+ny{$8`-d_>04h<^NEAgL^vcY#LfmVGfp0P?Y1LF;!
zI&+W0I2$GkdcL1z_WE<xj(t0XY{gdc_^xQl4Sl!OEZ3uNLBq4!iMa27aF~Ht;nK^h
zbo2@izL%+^?#w@u261CIfYGd9)i!aV0D%7fPSayZJQ2TF57kPvvBKsgUd<$Zv)*n2
z>-zpQDG632We!W*migg_>IBgG86N}Om-GyNz)~tRvsHe9#|K)?`Z^=*<ZgT_IO0XA
zq^We`Von2w1lr1YOUI%4<HntWDt_Q5kI6S=xW>q~K#e*-zr?5kgYv*E`~x}6pI_}W
zof!Gtf?M%sx~eLfFycm#SOx(7aJ|~wEKxLes<kJ^mM!km{XQXI^Xdf<JlAu*wFZim
zNlv<$$jlImUAx8jSwPMp$B$lqueP@uhWej5i1h8hM4#m`tgG=5M-kOwfWrAIL|a~+
z<p|?GGIb^JNYEHjnl8z-nN1{8CG!gwWm+yKd#y^w4574R1e+J@1tAEC4#b5MqHa!a
z--<53^|UXT5ZC#9XHOEh9zo$|0d=J(RHa<JL@R8ltCLHEMyn1K)reQ_OpKyr`5qh<
zpE1p@QkFqTANcR4Wm4B^&0c%b9$ay?a#4l$S>P0KevlW>lN}AnlxCMvdzaS|!)rDA
zPNy^IezM(bkp1@&G<|Ds^(k5*cMx3rxW>j@=Kg=b+Cl#F)s8bnkcA6r1_Vm*q4N<=
z5~^<Yr`inAG9Z(S){atbciDm~A7^XNc-~&wm9uldlMTM5vbwYS`IyO~`8U-60fpZ*
A@&Et;

literal 0
HcmV?d00001

diff --git a/public/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/small-s4-code-1.avif b/public/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/small-s4-code-1.avif
new file mode 100644
index 0000000000000000000000000000000000000000..56548bc6df1724ee80dc4a13928edeef1f744a46
GIT binary patch
literal 21467
zcmYJZQ;=xU&NbS$ZQHhcw{6?DZQHhOyLa2RZ5#hS=ewtF9#%%iNHS~YWmT;e0000S
zQ)dr*16K=EfPZFVVaoU)Y-3^YF9x)?F>yBd5C6{y&5f-c{|^EH*jpGm{}2DqvshT$
z8T}{tBLDyd0QesSnEZbp06-D^ANS9cvi`>a2>c5<EG%sQd+>i%)PI4=f7AX|8@Mts
z{-@&qC;ut`BjM~VoNfN2{<mA5g}tNwKOAXcZ)Eq+!x*?S3I~9|{6|NzaI!b~4*&oF
zdi<Au@ed%_Tew^NhXICwfcWSBzkHnkQ2#prp9c6Jg~PzbUHCt=i=!=<jlGH4e;-0z
zMixf)TuvTN&L%caT>o+`tSt;2J-7_4?alu?FI)?IoBs{&-z<OwfPevjK!SllL;v-{
zvT!u{zf1o+?q9L>f5-Lj%8GI}#s<zv08mhvWc2h>e?VBU5SX5e0Ht}{-Axr|r^}?#
zCP?L89zhj(!v0i}Kr<Mb(Zh$V4H#Tx$JS!f$n(FUq9N^h0x&SF%#z)5%#mY#M6TWO
zvt{WaY}*C$TsnB7WdkbpJ~BK8s@xnfTU7M3D#$)vKw()?i+1<Cn##(*v`!ItI50j=
z2R*V1XTcJ&TjP+_waA@xHBtgteY}=oHLUC7cP;}L?3d|kkl_Tank6NJOnQCBcMau?
ziKbd-mrut6+j55MXt5W1T8|InhMp{lQ*Ozd5F|n0T@bv#t0h(1g&$tprGb=h<)^Eb
zGmlw~rTCL$(P}KMYLHjtpnJBj?mMvq?hIa9%hb=q-`oR5N7{M0Udb!K)eenS1>l?P
zt>&^J1SGFNmp?U&8wbX41G>px>>&`Nhjt1<h$?2<l=kFv!2z!$+ki=13de<@l?*Ly
z1Hp%R#>SBN2#6F?lgw0J&2**PIC_o?I)i7C>hZi#C5?tl?3osWR~s5|Z$KXoSQ@M?
z<Mx_928~+GuyZt%2&^j=;}!>u>{p#AY*h7)wnXna<Qiz*-l)IwsOa^PrMIKOnERSC
z(AQHT%EYr=(0eL@`=J#}n_{>A8MG*=I84UAO3~rU6$dO}b+P4cM$xFl@+<K1tK4mK
znB;4J*xTCw5b70poo_7Y$=fuKfV|OVLjfQ-5q;8&>4UjI5!<!xs9;Oc%?<-h{Da&u
z+WU^9PIJo6<&nBGFq3QxfcB$YsI>X#=WwEhrFQVg*_tAsC7>K>ikk3Q(MzFEwZjW&
znC?46a6G0h6niI}zB2uGlJ-`42HDPF^s8Q=57aODV1<@zt%$u#2U;_fNJg`a5UA7P
z3yXb@8(<MOd(pe5TJJ&pm`90q3eJaXNs&?-Z?Qz*6(SJg>SsGabKR^QPpPcijaH9f
zAv}ZkGofeyl8an7Prv}j^IRv{@8w-SI6on&xewQHRRr3>f^FF0%8ez4q`=0qDA1w;
zhbk3CXHu*8Xri8#NbfQ>A9JudKBzakz2b(n@Y=FL*VHp9Ad8-Y2JD)I&_rCv3R*xV
zh|8_e&Pb&RLkD&>%Q6N|_Mxj^N?UOK1ZcT*_I&!Yya-ia0Sj^l5|AQ_-JIjVb(Wa(
z%qLut)PXHcicUE{?X@)@4V_gXW>54GZXpZ_r)uq92s4}a*6oQjKmZZ<C0*@8p4STe
zW|2kL;V`@&IO)`XJ5AdWJpA&Z7vGLQ%vBYIDVTTqd^J-NA)e^h0Z#1l;ZMQ21F7t7
zZygpx7|@1Y8eQA%QS9AoRWs1hUA%Z{E(`l`wWI`Zlqo7uW1~JwaxD<}lgc_^qy<4_
z{^4e?Wiik-gGVV99gr|$BKo(9llGSNLR-B+p3uS^A_iVvYl;D|LslrU?w?ViwH3D2
zO1zZ6@RD8iE~5=Ipb=L$c+4zEAF%Y9C>BP76)#c2yxC#BsT0#S2*k?C!qX4QFteEv
zM{~eX&KTuO?p@9cC#W6ZEm-d#N_jDJR}R$YfYWt9G}k>J5StUTeC6q*C^*l1`}9c-
zN)}nQqblEu-)hX6Y*4bsy&gN#(bdV*!Q2~94il$GXPb973$!H~V{4I8M_rw2*hH21
z*ZlP&qQ<yVt5172=N5MiZ~I3l;pv@C^l<op99+(dj+hfW-&$2HL9iZ4BxZmZE7}X-
z&o(+t@@E#6L^Fi2Fx?C?oSvOag05vxAm&^zHyUx|#~U&DD~j9|;~L6T_EcXC@{1|0
z=S0TQX^@PH-fRjSNpG^=&Z*D_=(>FUUDM6qP8)>`2yp6>i@UbfZiCAnO(5%`)acds
zJM`gYqyh7GDGIHAOo>UZn@wwIx|F@N6F(VcG)?>nr@J7_+fa$D(obBMO?;d(y6<EZ
zlA1&N`*f(}=e<K1o1AByIf#53HM$0``3`)DJqs6z!0ZDhiD?17CHmUoM;S3ZnydK2
zucy+%(R0Q3UTMsEqjfqy7}1xIR4O%X-FQc>c7WKvad&#jQ66|kk1Qf%EYzg>YwFVw
zy+ZyDy)$w{<&sW*7cZAh2YoZV4F&uHh-QHD1!-RehTlZ~9?T(9=1tbL>X6@4l_97M
z)=(6_<Wl6+u9cbo=|kr^8T?4Zgd}PKk~MR@uih8vr#hv?5TmT0d>o_x&Pt^lqcAL0
zIPY4V-2=r~SR*~6wnAot`s8Cr{FL@tf}mO9gL7AbwPA>1&&REn(_jeX_78^Ac%)if
zD^-M``u=7ZdTXG!I!{p%+=?Uo7GqEWufpMcc+#b_VY8MIxVLFt2i84f@_)Q`^d%wB
zA)75!`C*8jvIQ6@LMRL;9(lRdewT(Mn}jK$LhxU<<ij3tKvCN`<iwhUYishw4z8my
zHf``HXEVbs&=;Cxi2M-V6P|rKw%`Ge--`Xgdx!TvocYpc9v_)e-d)99y%lwj0}Caw
zSfVl5HdR7`cR29CQr=@j{?x+s{jQ#t_o|<0mjsTSc2-WanL)m5)XkE|qGF?*PhnpV
z9jF;qd9`J^;`9#Vzibd3pGQdin*CVNwwn)&*Z!v0+Kc=O3nq;PkTJlbvxQk4CYb*l
z9zK-drDxg>NaP;*mA=s?V7-u-#$q#n7!!x5dut0w3&7}kGMZ4OfCd9%S;BYj6(dwA
zA3nR_{AMS(oAHN#h5-k;!$MyX%*#tk=@LK#>l!u2HVf`J=a^QQ@~*=Cs`G(Jm79t;
z<<DV8oI$ST2soYSh6QPMJE*ssZZ$bxU2yQ;plcuQ5YpkI3X7q8?G#xEm%~#WN8i1&
zQ(39nth_lFVz3&4|44Fdgcs^XqK%a&Vy&FnDS5?w%!`RGPMEJo^riurc3DrvtgYCk
z=&W}#*I@5UXSn&GRm`N@R-Ga+NEGS0A)$$>mUcWCyEZ3*uAGqum}&tazqDj*g1!9;
zEfVnz&6cdia*SvuU>5vb+9!>G1h=s_LTj`8r?0wU<+<4`7}CNf&4W8QzJrdgEv&5$
zo2O#OmOdQ|pe#H!{GsvK9aKkL)g3qF<>ppx;vRgZ8Oaum@0kf&)3k^k^6Sw9p?Pdl
ztY~~ddLv0$iZSn$%i2!dbdXspDt*scbNi+M6T1@}bXb0bLB`emmb<^m_iiO2*9+Vu
zr#WQR-AyRUrU|d@&za|#+GI?|c2~+%?zN#j=A_#v@+r|!e;v?7#C1*%8<#=TC7KE)
z1jQ80tMMD6?@Es6r-R>OT9E)b{46sJY*uQ7GA4FgwzU)xgWVe-!SS66ckv6l1x#F_
z<+K9}9gOS!W)mRI&^q~dli3YTIQMM62712Q94~`E!DgI4m99s9%kXY@!TQgh2W|l%
zT`P~MZ{t%i&J8PI>!g~rDCap$Laj{O$0R8On6cPpz2C+HT{g0bO%w?f^jD-Op1fqI
zn@`8qNq3BNhjf$eGeb}9$H(wIvJ%!wqM+{Kc@SXJ5l$qeYVr!Tgbw@Xgt%$U=q+h7
zal$+kmxW)%;d?V;NyS7~Qh71NdI?HY{|B7(czby^qi<IP_)A5<Z|kS4VXoldjY!)~
zR{F2bTxOb|_|nvUL!9X-?qd{-_Wo>X!P9=Goz#w87&;%XNwt6$V;Oy`C-C3kDo#CO
z9YxJm3WsN|sm*&xHX4+IC=hO?<t<B$a~!{wzClvSSA>FhH=P}-+^%P4UnaA@6`j?B
zA&&O-(dXuRzyzQPd|BxN!&Ys<jFS#fhkLiX-6-DS*<TPm+7x0#b+PB64A#Wt+@7o1
zQ@M6k6V;0sh=9Uqp;XeCk!At+zF3_m{sxuJ4SI=ncSiR^49;v^$q^b?o3FZF?GF^~
zM~?>u4!5<29_YEkeky>3BvA4{<DUWB;%@Wf8H^L>5B|2DuYyf%$F~R{75aZv&7}Lz
zKKVJMUH__unZyXkEnh>1fw?1AYL_f;`FEZULVT$sHqR(Tldk4p2!IP@^&t6=r!T3Q
zqjkJSmOdp|Z&D#Py`(Hg-(JJw>fWWFY3zg1_0$4G_OB?~*xgf$x_X<t6>H6BQtF{j
z{^leIT*KVCUR3^25baPo-oSBK)*5na`M}K?_t_?Is>FoAA>O3sl=G2ll6e<td;T_y
zRhhwS+hQU7p2pe(%_rVp>Qv0107{joWFAbul1b3DH@J0!G7onvSPwXtZ<KpwQNPRH
zXei9yj0jq!VD_h72;h9G;TQXiPIp;|nX>@-<P1_W-kX|Kac5@qZ|H^zdI~UTu|c4e
z1s>!;P~tHW_h1LLy+RNF(A7iY+<b-BWB~_cm6)y!=b|V0SR_nh{gT#`^2svOn{8+<
z@S;mEmhW8G<aLcT31yy9X>h{qx#ApWIM?w`lTB@1r2;u#EGm+h3M?o=17DYCxq`|X
z-E-4VbFQf=sXSywLrRB}n-@o6aq)1;Ao;LMuyZN&#jh8LlT2kv`ztzg&pd?e=KxjX
zok3K^C1U%*tvk7+mm|UrCZvN&$Sv{RUJf~6fauvz`9oB_n>cC;1cT&z6;`5&z*{~H
z6&0jlEp{fLq=P4SM1w0ryQ?nGzD6Ym85Rr`aws3V4v9;4P;06Ha<!N33%!2SEd4$`
zR^QOYFdetA-EqctU{}@TLmqc&_=i6f`>udx<%9P7ap6llFF=<UhHm<u>-uz#^PqaS
zoNSm_ylyzCXxwzTWiqJQhVd$7#uOM;gi*x?0_Ft#QlCw3dQkW3<RRo~4i9mp(jy$t
zKD!r5_GAAooA#)p^o&hTa~ASr2M{mDW$OF&l(XMQ&B`T|$KRS4DM3yF!Y5a%x02#D
z@_k7Tc}k;Xad$~aU1_FM6C&cqzwez?9w8XT?rIO3hcld*2CJNml@r4@zTkzDg(eQS
z`-yj#B+Nw}Eyc(?<jQVuohM>N^X{bZ&=_16l4}(iWuXJo4Fi+XYCVmk7lfyjEKX|i
zTerZ{q^ra=lTrK0+1amee`EAIW=FE8vLcTkXsCY3b>|9s7shAXoOI2vTh|2RAeZ$P
zUE;VMi?ZQ^#ONFKkvZgy5FLL7Kt_)zdL|@3Kqv`MJdc{dsQa5dMgdo7(K`oBH;QFk
z7$%J2GIXD#BRmgRS8<C0sV#F-UuHFI|CLBxq**hNl`;GA^!$w}g*<xZwq%JoGe1;I
zVzM}5Ykt-0OZ^9If4;6<#1T@u!%eBfq#;A=%9Qogz_gzP3+KYqhG$f9*I2v4lsK|W
zCohkrmKf+o0#M>&BWnI^o6m{OG6Xbxr_f<QTvb!jeTbo}5-`C^JXe?ug~5sgP9Z27
z36XQzP#%w^b_G_$k&s+3IfkN9;m#*LR5}S7h5U>*6LB$Il`)nqKROw{Y=YpHMqem?
z!^J2uTG1sf*=Um52H-NhoyThjHpF8Mu1)dFjq)W+^kr!@k#W^t5LLd@4&Mg^_bsT4
zxJ0)xnvI?8YE?xbqjRBz+?3l&1n*oAO$hJln$Vp<c9v<~<jsVb4m{uIDQ-PGq@DY?
zB_Q@#Ta*RP@cL36(PL6*;Wcn5Wc$f2VAq_*EM~&44gwHo5%s<d3}32trE*Aph?oZ`
z=Nwacc!Ky+SV(=>Ik?DI%DO^{HW^Y-sI)Ti4>~l>p`@$l=VHmbW)3Dw`I$BmWz(bV
zK)Op&n4Zx+q<~xaV9s;r-R|84ku2P&y6W|+U&g^_P~x#!dWyx6{q+VBYizZ*_ez4p
zqRzBQbcd)nYaQ3iM7DvAxDetx1nXY|@45RI5WEd9?invITi+!sgL8PvyI-Xyj6Qn<
zL6L7wVA4K`!By^4=Rza<UIC5oTDJDxuT%A_mI$w=>=Oa0Ai-iC2iu~sbSMSs^LaGD
zsBU8(Y#rq%b^xBv=TF09P*JC{#0SEZ%0$8V=M#2e?wc-ST6`ikbkj<-#oa&0087H<
z#}PfBQWwp+)L=oWvec#}(P6K^+bKJRJ=e3XfyJ~HskS%`M9z4dwEpt%;BLaU3{!^X
zoi+urdTd>=fzbY6(2>+Q%&daWd2?it`w^?Y(jQcky#}X3p~Xt#4?!%VG3R`;aMZT=
zZe^%7V9gc)k}v8z>_c4_KG3P|W(`gtz#UOoGE~2##FINGCbBi^%&sm!(Qs#S2_wi{
zVG$kF9~lyK6*%CebY9F3G6oXp+Xm*(?I^!y_oMrn11<P-%S3hN6m8YXru?7RLlfz_
z0#nHa7IdINypeLZxo)OIqZ6Rkgie>fQIzOs?m-4l+*iZL;<wEk^@xe|fMkGCJY;{e
zgpp8_L(&?84Ndkw%H_qf;SmN%-7bQkwiQFrAKa|=b=<cOqBJoi66sK4MY5B^0-yYj
z`QCD3J^~3B)e1?|88zjQsVd_gP!7{L(ykBoM?Sfi3dT<XOnT8}lj?y{M6WSwLT<}x
ze340v5)ojfxe$x#T@kf;vl`si92jwrtS04rHMZvCo(Wo7zyD4q8gsgM%p*Nbq<g9s
z`=PQ1!$PDI1wf#OCx#k_7I7ll#Q6fr6uSyh%D+j{C?2kC$Et+hK5M=VtEB0MJLzmC
zLNa6iY+i;aiq!@@K|jINQn^(t*lEls`cgq|;vKCYwrsNdyE8&~Vb(^jbHAk1?mBQw
z8s0P~h&{-HQ^*;8=AhK+%;J>r2oi_cV4wYXVBF8IF$);6PW)w5JbX>aO-+PiQNFAr
z7G9U7Okt#KNi{SfUpRW{>}__B7HZ2l9BLgg+?j)6R&^Lb8er}G#!Tt#fn)2cV8Wl%
zG?b(A8xZY?zQriZWQ6Oq1V7`v|Mv!G?F-&vlA^#sr+TpxwniQtJ^bw`L+#EO7DhZ@
zl5RX;+mjGNKLp2@09mIm?DIjM652kgODz-Nfoiuq1!p2u$i@N@x3rh6Fajz#EZW~K
z7cCL*52uGSlxC*l;vd_i*K8VC`3lxQE;flC3$dR@lwps~UeDa_o>x=(_AYcMFP^&f
zT907PbZkK0SWz*t`#N07ai#}z-6-Lz#$w-Rje~yjW?;-0fA~{=iyF@%PwoYLtIz<1
zJk_9L+q++vb<V%TEn+2!WjY8Wo8WU%gOh4&L(-N+Sv%zBM}(ma-;eDhzFrgRM!B#U
zIj;Z66BbANwrL0tl>*QnwsMMRH9HZw1I3Y!8+wnQzwZptG2}JbEvN~<8SGSA^Gwm)
zJJF(N8BSKIrm&F#d=&2=Mhk$S5!S2qWMQR)Qvt4>_Z!N2NJ4rtqNcLi5(*4gD;Arz
z_r1PC@3Jr5S5R((gebb$BLYit*qd))qR970o#iN_w;ThpZ(BnL5@3SD;*b?v{&kw+
zp7ZBx2A$|&Q#uQE6NCYPmIKTi28#1EZYH<Z0&x7@#{D?LAz$o1dr^f;BLsx<NRoyZ
z?a?3CmRnOk<AQ4jqiEKc>La)LylY{3OWr}mI<A+F@bqXn$W8sx4^Do)O!5UUM}gy5
zDdq4UWs591XJ1ZuX8u9n{7d3Z?UFx^63a6Kro?>U!RF>uioBJWyeT1{N{os=L8eTb
zq7H|RVVE>EGqQY6g+*iJCPsGsx#~d4X+{b+QL6|<ZY$kduyp}2R!9fg1FuPUfoFxz
zB(P;P20u`>NjKJI$McwJ9!%-xg@7e7`&m>c_jTJ_H65#4?rZf$u*ZG9y^%t%Mb*$}
zl|>5iEYL|<x2Xku`BzS0t<YUl<Xi?pt&Fy+7DGUNNS>h**p-6?`dTR-&3x6bHQ6ZP
zgK2EWQI|u)yBXeq>X<=T!xJIFcOTHh*cv2%6L=~VBpGRY<>GQ}la`=@0f*)-;GwP#
z%YWBwCE&gOOf%iy*qyqQt+!VBFF@{-c~3gq#a!G(USBA0AfoG;*lpI+0zx$y_DiM4
z%M)V|AQR1BJ;+`}9vND~qD3D|43YyurzrD>#X~5R^R2tM>J0$#hrT92lbr}MUL~NR
zg0%Xsw&nQ)56Q=av;1f;j&oFB2ngy>X-J5E#KVOv03CUI*)D#o*Fdq39aEF8s{?g)
zFD+DGp40MJm)$2c4Dma0>{?;_q1U?V*uxrKuAX-?N)Rf>;&h}StZ@K`b}7Vp!1F{%
zvP!TremBOv6@8xL%_W~z63|UvF#L9g67-|&2=hjr^Fp|i$3J*;9QDejtxy`VhTcMd
zKjP>0S~7s{NdO(v`jbET&=*CJ7D`hsgr^}`*&^ad`T`fr#3tVI;yCkWY`$kZMbHJN
zJxf8jiVx^U5*x&V!b^w6PZS6#M@aSiO@ti999HBT=2(T%2a8`LbDU-_Wph|<OGzT%
z2dvR#f-tW{NBBf-1w4W*0SP%mv9y`7CdoN{oX@Fzh<my0+A1g*#LAga8*DVmj=Y=j
zlda@Ujbnf|Gxpi?|LRMlfqctPNUV&9XOc$Bh<{+r?K#2J7i`(ERPtAEhDtfZQ-}aL
zbE7ki@H~RVGc-ZeW8_`l6H|qp5RkU>IE)l3N)!34PwyW`QG&a^Xu=^)pRLTj!G_yt
z{T;d#Qs%zNbvAO~S=<ZZG^&~!{kT!NIJHGNPNw`)Jh@(bw{oRJsOh@Cx?Ll52X!5+
z#H&7oedlLjD{L~$R9#)w_$I`{;PVNOHlT(;rzXDqE%hG@vRezEuBa&M+CTN`{|w*l
z-zef1n&~vBUAu0OYLl1E`5gcTg856pQmVkL*Nw6=%^MheO6;?{h&C3PQx*)a>9Rre
z4C`mVu5D4ghHX_4(WHMcjqPXGTN|~;K~Rwq!;sU@T%@CKQ-U#ihY1{{G(KY*;DFEM
zFJ^J{yxuP@OJk8wX$g0SP#B9@roHDk!%^?(egqS=x+Ua20je1BkwY7oFH=|TLI*V;
zt>z|d&uZ&(o|gKzZ!Ogj`CZghW-cbETH%V1$!-?Z7=m|7v`f$hM~PDM#q(v75~AuX
zAramq`M5sjx=C-M50%-PaJXR1Rw{`|5=Fn={?eK^5)wd1vDYC`js?b#r_DJh?dp~Q
zFUr1w#cADZ010G_QE>E}qUj#7nsrN0Ct~=|xjod4!OD0)bW9TEncAKM<e7~`BFVl(
zj7UA90@kezo6kY6d1MX!>Bp%*YQ)@yZukv4&MRYOml7r|Wy(5C1G<y=cTo6*Y~WS5
zM@^Ji?KvN9DpY;LeGe>6WaP5?xZGY|MoJgymjpJ;-RJV=+gVkyZkuhrNWL`oqzs9`
z05(Gx6Ql{eo81nXdB;JUh)F5gbDmCqFn(qEbz~0gmv!@em`1>aU6@yYg}V}S*(Q!@
zjQGs(_`SK*<c$SP@3(a)2qj6}Sc>E{dM#+v*&cuVistn=cNX>(RZO4Nea4O_x8T%Z
zWF+h10cGs5Owd7M_7s1)Gmq*)kPwXtVv~U_b+m0Y301Lp3n&iv8CQ;o6JPTpWd`yE
zfZA78OiX<77k!{@ofo?N$Y$1dhi^k%={AWZtzqYn((#P_V&2_lbW(lufn{ahh@0Qv
zJ|}IzQ;eQpqD>^~5bWUbM{Uj%InQiui#^_Huww%0_%a}_Lcq5Cs$1Cu5tBnJst)Yh
zI!^nBaT4z*$`tS#jFrpG$re0$=w@`{xbYvdMbdi+yCm}1*YPSTr~RQ?gt(ciNNn~e
z8hB>)p6>`*zaZ&}N_y=j%(RA!;U|cQu66g|Ka`10qhJ<d1-OVK2oAlzu1wWmCJP>N
zjs-7VKct`feQxf9fFKFrZRxsdK6l@fMg+@(zd+~_U%x^lZTmcn3;DwJD-;vB)*Mt+
zt4f!ziZ0)(Y8##=3stJQBk!+M@;#Xv(e`W3vn%}OKu{%`*T`6yxVJ9j+F_u@u!>8C
zVfr~og7z5|M=f8QEa32J-n;eL;KZP*vWf*?9GY8uh;+<5L}5(*uYfaK>Iu#?nQ--p
zE1G)5a#o-$13tbm6}oHrCA7%W8vB~Aj8eBt9fd3Jz2=K0^gcuNIJm+h$^)$a=m7Fk
zCaXVaajnTczVWoU_u+}2ZZnNC8~aJ`WsQS*hM1PV?DG=swyr6eK4<7Xz&makl1V~t
zTt<_{Ww%c!VOoQn+Al`af1SYm@$*YN9yDjSY>iTFabw%kc^uzs>vD){&9=H;`Ch=`
zp0N2m*lz@%X^gJUFUbUq^uM8do~3VR-kN|t+V|xz@dV?4mO!1@J&vhM0$u9IM!U<n
zRKbv)&P8U&JYlI6@MJ(9Bo<m#WF_AEK#5vOk^cC-npSBX%t0pJ@E1l*E!9*E7d@^l
zuKvDsO!tGk#W_a(WwY>+7N`Wl9<Be;=;Wy{7khp!=$7huG$lA0S-M3Qvba<;wpiWV
zK$44f=-6O|B0{Ne6I0qW5?>xZjiTwm#MjiIx*^#9yYI7ZOh6XS7?Wn~!w*2WITA1l
z@J_x<<{`~}bp!gFMlg|)P!=fsW+H;`uV!uEeT~#0*)|#SBV5x6il8momr{2zBP*eB
zrD`$vRVe1R=Q>2txm&)Uw(bGEwNV=fU5`PJjE0-2*!O+a&~+0bEI2~kODPX&rCU-d
zHC6aif}q2P72UeM&lr}TTV==NLaNkT7<tFOuf-j>?(govMp6LE3j2jM4TU1|@n%mV
z3)t-v_!h?G70JQ)B4*q^$A4!*AF`Gi&?&qVc*mJoEUB$u;RFUjYD}gw5IE3CDm?@%
zCDJFI(Imf=b#fiodVcS*yBDaIF3%9BNfYnnGcK2p<{&q<mhNNxm&Iaf5%FQfCe$?g
zt15-Cx}=4j@J=wz_U!B3P0VsY+^ub>nj%>7%oIob8ab!yem!fM4;Pn{6bMz}35(ms
z3um=zs3vjjC#ZB8C?qN_fJOR_#XQhvX$=tEBXPyqdr*W7_x@9t-BCMZiKRE;OfB))
za!I1ChbtW6wC>5e>Z)7t$*2P>pYs*P!8~0=NTpTwRDfguy`4Rm8aD7Wvb6;|F|#xe
zfO6&d>v6oc@0GQ7PYa;hy@j!R@Lg<UaxKNXBz_RzyKsG?KS{S9jmVVy<#&EHS7yM!
zGz{@lMNHZ!=;gM=x<T>LbeWhSz=6sKMRw~u?-1a%_fM3FTjqC|h*)wp4R+lrT0@<=
znbr@TtxnNJcZF3soJyctjI@%<{;^Jp#F}TFkit*ub8NIv<31-_??Wq`o4P#Ah@xyO
zEpw~dU9!e12_JfW-I19*HRzboYS842FIioKH8jh<VdF_i^T>A8YH;vC)Dxk&?M8+J
z<n<j#sOKdNssl=N*d#rG<cGf+<<u+BW(~I;GcsAD7P3n1seS3@!iXmCweE2IDZR(I
zW8aarxJER}Vz@gPY{@RjsWUt;!+XwNn!$biX<PG;KD4$%6ddjgt}o5>jS90UAzBk}
zm$V!qqDQK!eunBb$Nr0u{gp|v`WYtx0Eu}F&(&f%j!4*z3BPzHob1m2CL2e<;j9KJ
zuv#_|67O)gkjCur%9S&^s)+_XO&S7R9G|?ic|EW}(+d1pcOmJ<;*0IapwsX9lok{v
zKNr)bq`hv3g%L=F-i$@Fa%QRj3vxmo-09}iW~iZFzspZ{1_Ck}<Tb%Ov*#65Tt~j8
zavHTXu5LPR(d>c}j$H0@PJ3z+?uRO_Ha=^ckyavdRK29c-rEc_m7TKbTGv!uWIcd%
zGj=O@T!X#FD;noqFsfU;W7iCCF?$4BS^td@z@Nn^=QjPwH$=nAv<@HKd7-JLkC>~O
z!#T-;H+o8{-7nDK`u=mkn|r(ouhvz^o@MDV^4ixpepUTVo2GFCFcDC_=Cyf(fx)n&
z7aH5h%LhL}r!BQ;)Zo+2ncNMPnM}1N0O`3RR0wd=<rA5&F<)m5zXJPYrYo2XdD4WI
z<DC`Z-={eMQOZRXHlpWw!63aMllg|}A2)Zh2ry-6gFLIzHz30Sf`EF6MMBO%*_z?z
z?z|YwE|)pqd5q2m^sEC4)Xb=#Y9V^{82l9SWeFW9=}r310JR?jnZGAMcNJk%*hsYG
ze@R$OcoE9QyWP~LVQa~#tOQQ9;&=&uhC_Q*@g31lBCuVSuGE*u@5~k!gR?>yArL(a
zuyF%<L<4&!^Fu4bcdwXPw|`ga@44A<cI|p0Fnfh5Kl~IWYTHr>KQaZL<XLW0R+D0&
zl~2<XxyRR0_;v@+4-_3k63b^M)KYO9j9`%%<LNvLV`N~i>B{0VB4OxAknLFmL8S14
zd9Q}{1%!3e^z$yPw8d+_q7x-hu>Mr1oj`t(g)Z?KcLGt>ou_`B0Hx>qu?xJ5gL7-@
zAxMH*E`^QzHLD7v<BNF0aVb;t0I}|A^5zX71`BJ%RLtVHLv1-qP`_sDP$SXq#)`V9
zz@J2U@d*2Nt4=5gsamS&BKM<J0Gs4Jp3+#yV9L01856U2C9jD|?te2tUDQkjt|5ts
zRz=40gW<(qDgV5%hqkzxy&^=G0Yr@aUPF6n(la85W=vzU8eR&GKpZR<6!5;W8W6U%
zCbALb=?F`E2Gi0FE*J_>gRna!PIp4`8bd#<m1Nx*EsIRkQO>KUiU@NF1=F<v3+*9l
zQ8dV;6@v9I+9c)nv%!_!OCOlp!B?qC4pQnRkDIC!-*bzy?VV)1+{TOrIADmtC%BNR
zF2C%l>30xhtBSwNQsdapxO8o%dhaSiGGx@<AEd!g_iP{V(JD}t;!aM(n(G`r(^5VI
zZ)2ho?K)+CuA|=r+GDw7%5?7NY}3+^+?5)H(Ch<Lu|dH^O-?Cd_$Ur^UM(Vf=Gn1$
z2}G|98Ci7J6nvEF*fyg*&C4V}ck;Jlc_Ap&LJcHSEj2a3(>Y;yM=29Mzjx<aRplJ)
zE--+P6RiCCse@&(4XFftZz<&dE7fqX@H#M~5*J98<1CoH5}$l6L8lM>QxE;>w6hUf
zQ-iXGx-4f)ZHBFd<}>(j#R(8+Y3qlT;poFjCUM-5CY)EEOHY;;2-=UPI}xB^Gx!6-
z3z9}S3UchYQvC^)I0*udZx7x&Ha2at#8E~^*fGH6mOBH&zJ%%S?_Hc)6OcsDm+-GT
zZr?|4T7cBI*TnX=6C3pgfZir);Z0ELBhzU_DoCA%c$9W9MJgt}s-=v*QWF9kx*1Ff
z*BP;c`4U6oyvq!j*l<0GkiiYAoTsBj-tVxWFFHnyGZM-g<f_T8BA&R<SF{+TX=IU~
zg7a1eI;fRP7>N9V1)wKfh|3o9COYQ<$LB)UD^dl2nfJYM%2oFFy>JB=ibhJvv#qr-
z1nmL%qg!RJ`5sI-u$dMu4|@HxnaVN(RE%u9D)R_UaJ<%!Vb2n3wP=7?LAoJk(C^=q
zNU7AfwQT!vVD}6o0vTBqQiMjXVS~%haWuUB;~@Fsj<49Cvld8_LVHzTvyddP!Hw08
zEMC?qr(SEs5Mqwtz~-9Mrf0XeVRhNk7y@>!jl6Krg^J+=qCyoA-lOO)BZbGN-NIkp
z!3`4$6cD$wbWnrmxn;mFmQ3mZBlEa|`-SqIUGA8oEDk38p$><L<0yLJf+gO5s<1r~
zl?BS`1+|d;h8;gy{xxl<r~3{pso|<=MWeE;lt2jQ%r0E0`X!7o&{FKn;5gwes&EMU
zE*0{rk=?r|ro&>FF|P1mHu;X?hX#F(LUVt7X)ODv@p6r8RJ^^>?0FQXnKg)Gmq;3a
z1{Rip{)xwe7Q`AD+6Y^1%@8To&~HJBa>df7K8wMqH=iwE2_%ozzYbhSlB@WK8iY&c
z$wn-@GHMJY@PN7uai>$+rXQD#!$z#M9O)*p<xVR$&;=7i&$h^p<}I=s`Z)se=zqoP
z$fwiN?^G52ks8$3G0Tx*G}+9%A=}b0V^@LSw(OFDMvpCSOJb-J;yXFaT$-bgJ(FaT
zqmt0rSlUejqcm$Fg`DZG6xB>PTJrLV_26<A{UtG6jekq<o$6ge6>TzE*aSCUK;*dj
zYbZ4J@3^QHng%PUdj5)>6T!UDYKgTldVYSTnT^1z+;Xwwd4n<DV-D7UCUunB5naP+
z4g_W{H;?m|^K>)5LWX<?cJrQq$KDPY&G*erfI&5bSaZ;doGE9_m-50SB&@QHDv%&9
z{ys>^7`VAgL8p78Y@m$izcNFR{o>8W{{B^{)_<pRSAVWy@S|EkF!QN0_~mmKubv~_
zjR!V$(DcIgye!2xJaAW*Gtie|<M%tZSd63`0krVsUZBe-Du^TBrc!La@p^aV_L|*V
zWyMig*f<MSn^c-bg}-)eKy|rEW9-FoQa908QPcIany5e{Bkr}RfQl5Y_#swN-9M)0
z$$@BGXK9At{aLtS;vl+I{5g<5Wi#bM$n!^d{)Rf=lxPyJ3(qEc2e1PwZch~h<Ka<i
zM=zc<cu@RkTLIRM6UWf*6rqX~Wo+)`&$8vKHhp_tb_@lUo_&>jkVPcK4--q46m+4W
zw;&)-(*SEDru0BiBb#rUx2%uRQbg>2`GC>$7QmGm8IL)A&6R=GZa8c^FlW`lgYam|
zX$qYPk&hQ)Kf#>2d<ZmTwm0Sv6ua*Eh3b0!=@_hZTUz0i%q=W*7n8d#yxmH7S%rj}
z#)IYu-$u8PZZ>2+Vb~q8u0l>qHk6hXVyk7Yo{*9t;FfM{bB0^`)Sa(8C|!WNemfpQ
z7@ZPAd;P{U>d!$=D`$`>x1n2H>roJ^gOl@1nXNWrC5JCus61AlZJf<!W$^y<b9zCU
zvt82CBbi<OOrdIh(TlqAV6gt#bU?K+B)M}hQ{3zN+fUwi4!H@~@tuxZJt2c<KXByP
z(fjG(1Gim(<zP|1k#bAo{^;7nb+4kF9oY{u*1ZFSP{*cOdUKBRg)ep|;6-O$iP}Ns
z<^zs4wLCRe8`)LAQX!in__54=a*N)NTped)6JxK?8G=`Zvk!Ni+;*yy$s{XvBKlXS
zn*SE8GDWCyP=uII{Ur<M8=eZ6KXw>3EeIm6$gFH~WLp%JS~5dI9<LvOLzp?T#ZQm5
zQY-q5CX^SmEK8lvFw!154DrOuV~0Aov}?P<l9foV6!3axc6J}RLuVG#(>)AMDJGl@
zt;k-n?RxXAyh#^)xK|o-8OogQdv>_?Bv-b)*Ih|;)!7gXrQ#(fu$DyP$KK02NuW`X
zrx)lJLU5c)p8*6yf6-5e2a&u~-JOgxXBL)_tGI^u9sHzBv`h9~0|KAYexznKH8)Gs
zW0g4<NiLcS=h8oq557m#$4&rfxW5FOtU4G_s5^I35LjsAs1HdNwEbl&)Blp-vpWtp
ziRmvtI90JBi&!ES3-KT2OXYB!tQW9F3TeIP*J;-v$B|LQw4}!2F|=!y6qpN&IFmLM
zrm(5c$fP}req_oP6}7h_K?rSFW0u;+`xv=2UzX&X2yKxCl-Tedr2XfMPYgzKIPQhH
zq`IZU&W7b`&1T^n+ln41*`M1oG;<<2HpgEP1Rw%-uWl+b#~T{{%qGg1@6X>u(dijh
zX!B#xjiz=P@-X29v(A_dJ|$M73kxrneTw{N{l0}W_^2>MUSn@iwhU(s+ieo!=ja=t
z9i%}13b2BArr#<a5!?xo0#5ww!xBdqhv53jY^=^V!08F6_CVyFkda!+5aD7+025|_
z#zD^D&hZJ=3<!ZD9|y|j#`rYf@e!C1-BMRw#neBd0uvX2*ut6pm<EA%DhcLvIqk3<
z<6t8~AOpa3w~RbW3Sf(UHzmPhe3_N`5`^fQc`K%d_Sr?9ek&t<enXOEsylvKX+33~
z*xAj<HOz4+k`j;bYuQ7v_!!fg@6bygM~A8e!{ltb*DpQ_jQMNDDU4?)1DJv+Ob%wM
zLwY{A0+PPf7;rCEF}@CpxgE|?NQA~KCAXc?WOpXAQO;$2pCpx5-DVN%fO|ABd9^f$
z7kga;7P8^31cK<#Br?OiX$rE5_9d$8OUB3~#~KJc3=C(c1mvhWW>aK4SN*)EH_dM-
zNy!IbosVaira0)niuZ|lL*Q#r%TQJl=+6A@EF}dMQm}G|pY7|$z1df%^yi2&H&-qW
z(|)X`SO`K5x+L^2L8Xe?pro(yU{!T4GHct_PZ#>RCMD_U6}+9iAjKx0W08IKXyX_V
z?A^eVRjV%FG>z?ws04}bjQsQL+~UGJm&N@yHZB+9TJMRNOGh>csYM-QVxI+(x<~^Y
zl04THyf|S~)uKD02PVHSI}M1i6j<yNTxe2r09{(dAfP%_XgFgZc%z+UMVk8$|JFKu
zq9ef26KIyYD9IavIBqo=^OMXC$#r>uJB93>a<6Caqt3GU;m-A>5t+5<Qig$3PC3M(
z27DWp<`xlGCSsHc83b{}{<hAB&Qp%1iy045BvDq<IS#`6fzMd@!777VzGrIo?i}&p
z_N5yafB@s$8>hG;irL~{{asK8=xpjnj-FCCCn#kszl&E68(obr2Oc2(AuH7O;XECX
zj_p1&m&%7<GGfZpdIHUS-(UXrNP1GJE7&?RW?fw<wT`%W{GfE6N)iSkz`D0&dbFQU
zxTfubX?jL#!WqCg&7+EQ1Y-AY1fsUB$xj2mJCLRPopP7U`n3`PCeSA+)pX{4@C~eD
zCT@NeP;q^u^a1Fr5hI*_?Ktr8sI38`jYOD2ixt%+s%&tmJaCE(Mpz<@R<%#RZM~3d
z<4uNURv~+jFhKp{uajFX%%KLIcE(RO#=YyfEuS8#FPa|q1}br}(seU{idN*N_B<=Q
zs)1ZC0ClFSY-$jW63WKrbDZdc;mbehaj{e$oUPfnp`QmakQ6$UTPpN*lr3b<<r5Yc
zOf@f1y}!Q+or-wo7&fm+F%JLkmQ4m-$!0}luYJOyR?$IJ0RA-q*Vp!00^QYo9*%r5
zGn_jmDq~TLGzJXaJ}oj8b-c)ts=p;OQ>4k1YKmwNcHI>wu$)I7hd`g&P$PX<;M<@u
z=?RKStj)xSxevoSn{qPKQiO#eXJ!y@dR_~CXr~1`f4jO3_#C$bxlK>NBGPBxTSWg`
z;qGP7mMk&=MkEYA(8GM-5nP4a=Dp`W_`Mg$`TJ`FKD@jY91L)4DFwfT_MOTunRYaS
zv&jP`zNack7FR9_t!5eGx1NHi93>wmtgI;!YX|WlD%JDbk1AkAhvm@N5??FSNW^;r
z>&dv*_lF4SG*?HFOE<1obg>~EEhs`(HAV@Lljk$+gJ2#P8t~;eM2>c1Lq4@6%lJd!
zk&2j=q#6@{p7nxVd?@rW1qByKtY}vYTY~i73(9X=1>P1T$e>tS5m7Va?n!bI3rI<+
zrtPbjTSriRaEAcsU7@20DU%LK{3QX|cXX3;E@~UVseMWV&@X%oKt@qQ3;W6sih085
zbKY5CIiXSv-j3T=bUOQ!LsqGyfZr>vxvJj0LtYl_E4lt0IDS)CXK1>lcluhHjaHYf
z4}k65(zZ|tGo;%>AYOQ$Ef!e$8RpAnQsCbwvot3r|0S@}?6x&<+Vp5(jYtKKoM`()
zbje=kILhjyZQ(get!Z8D2mwx%Dm7gNf{D!w;%-*=xXBmAK%2W~#M5fDq!v|1!7ALf
z8ZIxoO0BJLc9?)(@Gi{}N}SWMut^j72Hk=iG*c%B)=JQVtCX>ac*OZecrTA}5gog(
z(44#)Zj{0~0_r7Y9}VMe9h`odc?s*aZVuLz)z{XGJn|O+ZQ+-T>S*r0j+v<`7+&bo
zSuG+`$pBE1wr{3X%PF|*=CMSo3<2Ldi{XKMEw|v4wY@#kmsufh@}W4T;vl}$E8QZ$
zL?u7u%!C7t-~xoXHJFp(NgZ>l6J-05b$cepEizA+ZyfXkhXW(sxk`5YM7utGYN|v4
zha0Z_D3Eq?lgOj#pTi=7&g*BgIBDmja*L{i9#_3X-ytV8?`ssexgulwCzxMvlsiw(
z`B_+IH}b`wZ2bvRoA{~Tx2U96{VJ2QCT+;Ubm(X=KU#PR!`=B5Vj7{fS^=|04HBP&
zVo{_EQ*zO06lfebW@>@t^%El1FLg5N-_Tm17Vi=Z`}*&hVTT|oAN3+L3e^oK#*J}K
zMam!1x`7~JiEQmoze#W^`qA2^iBh=S4$JQedU9i$QSDayGDJShd+CdblI{eLhpKks
zg^detkgvG_r(mfl<cV*WgW{0#NWP2-@$r6`pt;LDar0QZ*s7~WJ@<cq(saOmz2)qr
z+LN;#68~~7uCZme9ut7l@wXS{$ud+LD<@RD%|eU+_5H*hrXAZ$((w;5P@Ll0f-fVx
z)Dv`u%d`b8lFyhUhKB_--!2FMs2YZ{{ab;*yeldgjn5~TL)?;w`vmZgAWj6yyyX12
z@S0Ys!&3vPH0|YSisa*WZjy$q{OU~R%F#>Wojx(0Po3|aMhk-?N&8|m8f2uGH>Gty
z7=Py5kP%rIy}Kb76~kcFc96f5rM9?$c6HaSvhWhRPGh(<U&dJQ%ZG*dx<W#=NYE~H
z)!X{ZSTMc8jKi#NI&x*58aFCO4!r}8__gU*+XnYJMm4V*0;k;|#@S)ZV>v?Jaw4CY
z0;;<SiD@dUOma&$kT&g{^n7u>?PO=%-zBw@oDWaZM0YP{u_3uE|H0!rGxbO4dT1*T
z%eUyl>m5-c#Dz1W;59Wq@uGbs8~vS0T{Qcc$-{7)1ND!At_eft6zxDbvX2HU0qmwT
zoOiJ)`)uRFqA+IJ@s<(FblFztt`6df?q+~>TQrw4h!E+YIcs3$eq1mKPUHSQto%4#
zV`4G83baw?82T4`y`EPd*tP1T2Ta6t@gz-0es>cKOZ~bhMZkq6)u#%3x4Ud7f;`8g
zwDhC%>&QwnWBtrF9Tg6M2HAj!d%|NB4Sc%@WLcn|leAF0BUbG_;<ISa;i#Lhx*bT~
za<t>K1of^n?(yuHKoT7LkX%o$a*tb9Dt%xqK4e){2tt9Zgd8?QRH>90wJS4U;c-h#
zndZ1)<<AoCT^RcVF|w&cipwGp@KDcan4}%`twE{D@7S1#K;IPFujs;iZuX^5@9(8s
z%O9FGn$D-cWi@|*SEa3hrJFf58Ure9Ft8x<ms+s9>m^FqisduC`Zw``zOYQJIh>^x
zgi>OXn|jaP0m(1Se|+1i#Bx*$4PuJozhXiZ?ptfYp1hHReoGnKVSWpJFSkVw-v+zz
z#I0j4wT1K0j#ril#`I{KOenk*k?dv456~m$z7hhwo&(!ar_{hKll2Ps$a3Sc#wSg^
zIS5BEsw6+E;0lD?TPH-vO!?2$$)D3V`p5rd!Xa0dP%cdZ-s>tYJOv)n2#XSSHeQbw
zPM;mpUxLXCa)Y#<ZofMAxz2A;6Y&n~?@4jcUI-jz`$_N_uY}M)dYu)gdJ|#<Fv-7W
z1Y_YsL4F4jGrJ={RzWN@J}+=G85(*OM9#4b(`J|;pEECqJf~G*`|ve9c%9d2+X&De
zD%R5Q4P{<8;{Q3Fvi|K1jQbIk%y`8q=F|UtwLGY-<I}RVbVWGK0yOvE$E`FgEo~><
zPq#U$YRBcP%t?ymaui`iM-Grr0OkJncL;#EVOdP5Uw6-Jj>SOWUZKfKB8$p-V9|Yv
z8B-b65g)X>!2pg#eIUH+g!QIWkyI|5dw(RgIP1QxLT4{kj<Ce2yFy(PDaPpBFthO{
zBhM=KV6XC*SnC;Z(ESt>Y+VL>pPHlP!Y#^PpdU9C+A<RD;ga>!trP~WNLxSFKT$HT
z`n`0M#s@iu0KrvN%skcwU^<(nvUqYNvWHNp6yXQngK6Uf?lKuG0g&GVB1O*h_Hqy^
zGq;Hq?fhwwXqbIMYL>ts`=k8nb(vEyV^6G{&L04q)`SxHju#op+{F`FhqBvDc94<h
z1~^3_Z`MLZHP(>fq{my|JM5R}5bL21<?obj=RAh+N4xxNwY*?aX9>Ir@ij(wlmYE)
zjSFe^6YPZu(>xzDr=08oEf_y*pe4`E<Ig$uWMioOp{$McuUIs3;B4n80^W9mIVWiY
zhQh<XaYq9&3hrMy;DUq@fP-G#2I}j*G%QR{omRf~Q2+7h+h&2>UJg3keYC;hIBz&{
z`Zc;M8_|{^Wjy}$vJVA)O|N>drQL|4P>dq>hyBpis>f5~XWk96HKl*M)2?UHwYSq0
z&8%sl^adm|e;xP8<HuXt#C<!Ce3s?jy4H@GgS}9!T)>|Ib|sfMv|4A3ME?|+Z6Jb-
z-I6BR1*ayJ#a(=&vxfmzK&-01TO3KqT)nZ<sw^IO7ob)0q1W-$eVXR!fp0j?<N;Ge
zGB?NoZ>e>5D=4$mP*Ii0Fd%#xCzpDV{LOm?X~iw6-0n1v@v*yceM-8jp{CmrzdaAk
zHtmjBf@7)CaBJgTYPZx?p5~(9{;f#abW5gBMj?%a5d^OxypV^a76hT|3d@U0#p~nd
z6@VZtdf(L8yBisTEH)?|1<@pPvl8p#%P8locPSn;w}e2SXWRhB*~pH6N}O@yh`xK+
z%ExLE6)c%1v{?1?n5F_+NX<<gB&sY87I_^l#l@kI8SI09G)|Hzn{j101ZQbFECLef
ztZ3da&BV8y;vd@`99SNLIAIfGfv;D4Z#|(a5;dKB*}x`<800$b<KI%UJm&Sr8lM2t
zo-%hsE0i9K`Quj&XY5KBRlZ6q@-W-+qbr)@O|@kuAMoy4IpL7M<MvRbCLjQSNKVOE
z%h@}3q`vp|gSYAkR^8L5w?mYp+5YgR9KCccY%&vnE?qE1n(?!u?xZ*utyt&hQD~SZ
zscXq`t%I*HWOQ+4x~rRj134TR2y)$W$b{v#uNzOBakVXM(*qLs*hB!NXaw&j9^9HD
zhZYi-$&xYOo~sel7>Uc}0ZI#)>)S$T1FDDwA>%Wsnaco1^JbC05>TMbFvl^YT=iuk
zJ)3Jj+VADy)dg>yA)ehf00R%M-YqmN9_>6~5F0>recn9X1CzI2u}tc9I(hiP7NEn+
zfW9eTL$AiY6yI|h2e4P>$-+|Ol4Nn<K+jLw5BSH`9~)bT>!Gbd5RA%yz+=i>INzn<
zG!Sg7hf=Z{ev&yVkSRG+{zEDM*78ZM3Z(i)=)w(h&AfzAM#f!a0!xA4lHMI@(S2oS
z1%))OIorr}_H#`F7EAm%aD83o&FbBdms3L9sVjbxFyQ*j<&*$1hWIf-lIjk}p4rV;
zizk{MA~Y4tHXNRo5ELiNClJG6sxd!?!DRg>$jbuX^G@cugUKKE!@r$O6H{euj$m<(
z&nkxutmK}odiu3H>%BfFflDogESt41=qcpJbUL)(T};hrgqS(hT@bLFE_-5Df&kpW
z?X?y{vD;(Re(((lg)3u(=gIsYuSW;?1r+#ed-am(1C%Ez>yk)qZR@uMf)USIoqp*y
z&9Pw&a@LLIY&~Fcw66&jOqhYe5=d`2GSiva%%p}4Qy%a5hX(<&P=*MfM)|SmO4(&5
z$x{x%87amFV+HeCV*ToOz%uZ8xr;C9+s+|<38S!=NCxb&VVws*f6;n%p*`Mw`xf`k
z?D}u*2v~jcV7h_%5?(rFc}IS99=tY>Ww_ez6O2&**HfAS(KGts9Jn<x7BU0a>NZPg
z52mM2eU0z-4S@4JRe-iJFB5`Nh|u;@ECyt@A>aQCAQRv0teFl^uOy^PO!?dG43t<M
zeO+D%uHQDeUg|I|kD0JOn@rLc!G6to)~{+)ajq)3;x0we`lJu~y|e%Rznj^UWmiCY
zZW!{_!<@!jOsEydgG2m*OD+4`xgj?Lf_7X^I~hTbpDxc~5YEMEf+3~NXd1+wPALb9
zG6!ci-WC(56XPaKLmAPyekuPKO6xQ{5wvPOA_yOa-@Bp`Xmg1&S2apK<Lh2?kkG<F
zs#<Lc48)yp=v0)aMB%;!$2fwH^(Q$<H&4ifh+zZUA|7dly?=PXbJW;_H=&B}`9IS{
zg#lBYqBemS^y@M2xZBY5aPHKxA6}8OpF|*v$LF}3RnTGIElLp_FB?%Rv`;`*$murg
zIlfbI>wr~uWJZFuzUE-TRi;h7#EAq7t0)}3W`A0oo1Me>8n7Mdom&9v@x+ilgSo-G
zL1GdNgB5W6!n-ykP-HP-Ri1r`>Z$p9w>{2;?qMUcI0m26NtjG<gd0(Eq;lJawqis&
zTDsy1V<r6_9e3W2pt|+k$bYSZ3n*<=;_8zHNHNPdSUqTa$R7@@0xmRjmJX17nkrl$
zNCmp-;Q>3|BcICP&1b+q*2Z19$Oy=wS{I~UJ3cxPsv~Hm$NFcshf6Yk;QQP-jL)?|
zl`cm>q|8L45lWlL>|Pb|E$06C3(a>c=jabo{D#GQK<JLte){tQ>7A2dN*(dJo9b^)
zp3}6XH7YF`CBm$e_)fIMp^7j-nGcM1lzrFgZ{n(=|MsjMBCu3~Su0p!1GC;)wx@Sk
z4!`I@qLIODzo2AYpNY*;B6|A2N>1<m#ace-CJ(*{UUT@ZhEVTryk}r+KGz~}Je!`4
zZRBISJTY%zZo2XYn>+5seHWsPL$-OSBzcSvo?qnT^(%paQ(=0%{0rKz(+xJ>1?e3@
z+?t9NmtZ|GrTD89pnPLX;Vc+n2YGg>{K*74t?BgAgXw2g0L9Zws-&3~&)Rj0WN?Q9
z=SU5#=_cE5Dv1FH(}`mh4;Cb-6Fxopk>zf{%m05{!0zwQi@uWZOWZ`X0vRB>89TRo
zmxENaSR5$H=j;{^<K}~wX%uvR?6b7^nP^0U7uY@+`2@_+QR$Sf)OG{2dyL4UbsP<a
z)7H0W4#@By%iJ5)7*$)?t2po=540jLZYp_luRSZQKhHoqEA>=8P@ty=J+=eFL+AB1
zXGRK>%o(icex-R7Q8ZzQ(STRdV|co6U+n(HlaZ~wOzm|w=ZqhAnN9IXqJ$zq<EOeo
z%P|>S74~}}Shs%SI7z}fS8?A#4rS<W66`Jy^eRw)4EFbpON=c&(!)dn=2Il?>T_$%
zieuc&A;2or#5RZpRD#q|tk76x!M>SMYzxEK&gCygdWghAiX||OR4ru>4?OKiAT|4c
z4Ja@t&0%Ks<%Xt4mMUE3`Z8ob&<l`lXC-RXsV!%h*AWn9zg5Ykdqj>C(uryooGKb8
zZfE>5J2g%>;>-~e2LHifSU4N_9*jpDSc#gTrdHBcpRo^$@%*=9-3AO6mgTnram3B*
ziUfDE+9x9d&l(`1$vPuIWy3ha0Bn<fvRg=`{ODSA$zvOQQFj}#-N`qj#jb7{tGdb;
zm_mu*<38uD#ih3rVaqT|9&$hdyL*~(^4ixqd5u{NAA8t8w6#BX@2A5B+ke5y)z^CF
zWUJWE`6&}!w`fMHdNTWtKMJL^Ny=8F!rq{`v_eORHrk47;@X%bpKq{A4)c@Xnib&;
zc7Fy&HV_Ge%sII00~Jk2jgS)>7Or7=Tq}3H^C_~=Zj5StlxrWbFV&K*497BKy9UF%
z`=y-Z7Hl!{T?Gu1t5%Amxy2+AtH$~PEP0h9DLziTAjWZ?4XXI2C!xcD5>#nSYTxh>
zI`Axvn=<EWzMa7^ijY+yr#n^TVPjhnZ=#BOPjbu(YH~1Nh#10caX=fz%D_vxPP_oI
z;U-?|C2NSy(0?vRNzgu)(oUTlwF+k>i3W=<KW;8oxjoLL=Cm2`1OLcnBaiGzeVsBB
z=+D+ru^+eAk0X41t;BZLOaI`fK&Y@2vb%Tn6?Le;Ii$2_GriCf@R`iEN35}|QGlbE
zPZu#MV1f?xBluwmcs1t+V^bopxQ<*|N?C6R5d(>-*+H4bh%-dSxJ9@GP<t5uPh1UL
zT_|eUg44vg?vT9a*r?qAWnEJJ*?*515AOrH!qQ4Zq)w{|9OotBuPn~-cDX>E;yfz3
zp9^bai<(<#*{$$YVgvV=xM!vWwa%$HGrswB_I`xs4&Qaec-tzrTStx($TMhmiF}zI
zQX1Hn6T9xh8=cSIzL74}1Qr!i_pl<5k*t^;eeY2M15ryG+MJBd+<eGZ<LD*-PXl%!
zM*~<_|KuWigH%M7@MdKQ1%w?6^jEh+wP()P5SmxfoDYN8S?D=CGHCX<=z1b4z@ME%
z%)yz{=bZc*AeEtObe=(0&yJ?#r3)9?b!5GWt<wSZh1ngj==NorO?P+;W367<D_>;E
zr`cdRw{tjnY|3k6TKf)c<2dINpw5>#)hg;K#WT}Z3XSOL0a1<KT`bk>ZZ;-g;n?Zz
zv9Fy6=AgUibxm2vP%MW8!@A96WfaME(2#_M?s;S=6xhbdU(<lrUin0;Vu0s#WiMAN
zKaXqpcP-^-cgz2+q+pM?HLGwIKP}3%nOh(#8@os>9TFt-dhXi_k=2|V2UC<ZKbqga
z4(qBYm%|wizRy*OsMDu*{IhvA2~<yf+vvFJLz~c7%tyWf04Cp%lmn3f9V&Oo%=ZCH
zMNgxpf{?FbYpEX}<^k7d_=hz0)f-Omst{re#?i*aZhTF5?jNV}!pcJ`Z=MV>u|AMq
z=O#^7?6h^Vi4p$}rrLo^1_X}}O;$2!pPj!R!&cQNqbog7s66@rN`xEH<i9v$UcZf%
z37kY^NAO0_ZUEKSR04Jn_I+a6a}tDe;@}K)+K=(h14JlsDYA^%8p3~WKM#NF{HiZ6
z4bNZ=1;aL7FIg%{%kp3RcGI=)^v|0A3@pv^pgKxGDedWSCN3AH;tXDx%SK{G?qc|e
zD!~{W!OWkh&I2BjMA(EycOxxirvJz3ZVDe6khmZGZAo+|pHbD5E+H5{K!J1PtJ0?2
zJ|7+oC%#XsNh$ZEoiP0cy?MVZ=bzS^t-Rq)$=Ao)f~-Pd5cIN3jbH~riLP1faAa|7
z$e<`tq{XCu-Bh$EYXE!#2NByEnBj#+T%a;z*BeFv2LSM2So;snt7t(n%tOb;WDv8T
zzbgj1S!dJ@8WmB8OB1V}DIlH5Aun7tP-6Sk@H9JE93B8Q_3z9drcDPM$S2MfRlQl^
zR^BA=P}3%J)|BM-Zx&-%oc4Q-+yFjk^5?A7+F~qGPe_|7f0chLLF-d_+Bx)bo>7(x
z#UG^_x`NLvfPruQCouTHwrRKuj27nSNYAq&%0n~sW?Im$We9+3+gG-7Tc7EPMo@o2
zrXPI4M8V_eX5g6U{E*WQ)_wZEopk{ZLoY<m&o4Vzysc2fy!@X;Z<l)n;ac!zR;>7t
z5ZT_o@6gktpCS$5itDYih;c#OAWxv<ysY`~Io8Y|9cc5PAH>Vq<z=1sbX173pE1`K
zRos+5%0u2lWG9Ly=5DQ8QlkgO-Dq7;cRjZ@HR%-a87C2ojNB&#s-HB9uXj}2tOkOe
zYlNl(Xze=ChWO!|f@4wt@Yd<#_+*Ob2=4P(Ej^=5>6!c)c>3yiMx?#%SALix@}AMl
z&3~2{JX2qz0bd~YNjW-aLDXSQH=}-!Az6-Un%}QGF9EGRI$ylmLA|KpV<tTF@16!A
z6v&cd!>#S((Rk;8D0Eb&Ba~I<ey0&>HyZOoyJnE&1y+U~`Sa0N18%fPmlwNLkxwVy
z3=a$I4qpVmlzSMno<_2;-AewZvVsm!yurqfs5L1*BeWmL5y}}pwy7De#5c~m{(9{Z
zwKkQuONjWvs%MhoX^CDkui$xcNP8*=%0KwCXTbnWB*E%Wc45aS%Te;3KZ9K7jUqsM
zRg_}~%c>x@@8|AtAm?Nnk1hNfrMV|epNw#l;_!P4mkjIn6Mek%&J(&CoP)WZfz?&o
zR3<9FJ@+DaB=R#fiT{9Ew!e7l^}e7@=j{ix58c@1w1=Wg4qC32>qqCl>Xd}k<tMS<
zhptA+d!_(E^_MfaP?x>!xFL-nw=Dc~COQ+y_2voy#!=-Rxq8{GAufbYVdiu4HoyB5
z*1FHqJVSir&Ns7IISxYj{w>BI|4IZaIRt|3_Ym7-oPH6oQa?sljt`#zup}2KEH8h}
zmi0xzzW-5$hm3BNi<BLlF?ddA0s?*Pe-+kPvNF~v{U8N20fFNN^)F!(sB=@&PUj%7
zU_8$4=9^m&Pp`Kln2Rq)l?2>7sdVo}9_y(XJ@_s_*jzK*c2uiilaqf?&abWV&SoN-
z6afa?a?KFxlnI74wC_`kV9tg=B4P>^|Dp4-^*gB(n|E`JSBq7v1rG+^$M&c~TuASg
z8^V;kO|}XuDtF2<^p5KSa`Qhlt4dff0Ws=`r;6DSvjlDRG<V7oWQIKzc^nNBP3?3^
zMSqlZ?pb8q5_{?1(R(~N+J|x5&gh6a2SM)9B(AgUBLaqV*hsR<mwM~EdUm=H$2Y5r
zMHM9_za-m|;TYmaA3#-B+5mGo-C5g=Fm{xh?@jOEwLO&9v;vY<$aIm62F+V*J|&ph
z+!NdB4<9-F9b{ZkCfXQ!M3e1(-&@r33VtWY^$^Lj@$?wf$g{zw2&X})rpmu%02g10
z#MEwD<)oNw3dmwpN1JtK2h7+7kIz&>0h2|VwJ#1|R!z4ba`0%IN@BV%zUeF7k-tAQ
zu8@c}3;>%OY|0)LP*~?7_5F^T%jI^AUd?o85CBzH6ru~J-+&AI+wWY;e?~v4=ikJL
zRXYeZK7sOrdr*Ui#&aDqS{W<uRa`rL-_(tVuqaU9+*f4yHnu84r``K`REm8`@^FD&
zl{YdmPfPSTs41V6iioX{CojTes+91H)ciczg@H9`L#dYFG#*-qU;Vg?)TU-q=<GL}
zFVjl@ORxd}Gck>Gq}epHZN}9CIXceeo+_W(H{m(EIv$ujs()TMp**foa+;?rc3?x3
zgNLw1MA3J}OUbd>`x;C%?&Gt^u@P>Z4Qyb+z0u<aE@%@l!yqB132xnB5nPt8ru>uf
z$120ic4uCE0wabnG|NfLfX1TO0U1ehz5wvJz2kWshgy*cFBiUS<*818-p!j_K<f4)
zGYiz@<*0faC225CNImp2k~^~3DafFW9uw>hAVH`MctH2{g>RzWt<|M!AHC<NJv_xA
zox1ZE2WKp}I0;%YnM5sH`*OJv+6q9G@jF1aog~OuNs>Zw4;jWRxZ+rN7xVLDL5=-_
zPwI#Y8@oOBF-0Ivc4@7+GG*i5>W*=7Hbd20qP5AP3tsYyLH-E-;*b`i`W87)t&{HS
zeQ@$rZ{{4((x0Ndt?^ieJn5Fw&5!XUch>1YX_s`=@~|Y5?MocOnBDcLNvIxntOom|
zYe4y4?G1D5j|h!wPNGW)Fl`Bf5*&oBPP4QzpwO`gXhpn?YjIuuIJ2*I%&$EVIzJGo
zIa<#&;p=y~hi&Gg^dMs6Uxog0;o(aD!#4f=`T4JILcoZCxhu0T@q&jl;=~Nb%c1Jp
znZs|2K3x49^*-|(f3hBsKYF+ktGfkZBUALs12mK0Vr0ws!G0Z4uBQpN<3&3NSLNpb
zQ=TqE%WC?52I+SIj2F}yaZoUBCpen$x2TYcwR{`XPFW5%LP)95*^^AGGgDmk><9q7
zDgQ+rj=yd%m5v`a?K3QjqTCckCHPmpn{8LtA+6W4dOwu;*x&k~ve#?u$TU=L=vE1d
zv9)4#D_KfsU<K_J%(T~sXV%k-%*lF-1*gi6LcRGB%0*{Kp5m!(4T)a(e`Eg?epc0r
zc1I_pqd^Ye%zKlNgbSN_l<eog7^P}2MpY{|4?xRpSCBjo*qJ!420%y(Eg><<h(U6^
zpts+vtc#TzwBls3qXy73sq~LSU4Th=x;yW%ztr1W?cBUYKNR`aOlG9MFF8G9)DaCj
z7Lguyvq|~3t$&)FW?Xl<Lc6Aja5}I-vAPHcnt%V8)@f!wWm;!O*dMF;I7=W`T}MT|
z{+=ADYm1B>iT#Ju(Pl_^m2tCk@8oLGJHKBa4!asbo+BD%n*f>09=1eA&BA{B%NniR
z6BTXAo<V0f`ja~30)7p77oTP%Dc_P$W6hrVG8u6a&n#fKeI`34(>;7O5bXh{v`k*B
z%4DGoXRe+J?h@t_$@_S{?jvNCfD2;?Y?>8&*xweX7rs^lg^V?>(n5_mdwY~oWqED3
z{pHI-t1@+-G10a8ME2Xu$h|t@+AUFWG8yWcxFmSM5&#MZ8740;PYQrCGZ4@M2tf3_
v^$bV|vC&CZ&;Zw~nm8+NCsyJNQBy{BLgA)Ho@2W5ybVRIhit^IR_NSH2(luz

literal 0
HcmV?d00001

diff --git a/qiskit_bot.yaml b/qiskit_bot.yaml
index 83c1ee2bc42..df8a4a9c3f3 100644
--- a/qiskit_bot.yaml
+++ b/qiskit_bot.yaml
@@ -413,6 +413,8 @@ notifications:
     - "@jenglick"
     - "@garrison"
     - "`@beckykd`"
+  "docs/guides/function-template-aqc-trotter":
+    - "@pdd23001"
   "docs/guides/operator-class":
     - "`@mtreinish`"
   "docs/guides/online-lab-environments":
@@ -831,6 +833,8 @@ notifications:
   "docs/tutorials/simulate-neutron-scattering":
     - "`@nathanearnestnoble`"
     - "@kevinsung"
+  "docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function":
+    - "@pdd23001"
   "docs/tutorials/readout-error-mitigation-sampler":
     - "`@nathanearnestnoble`"
     - "`@jlapeyre`"
diff --git a/scripts/config/notebook-testing.toml b/scripts/config/notebook-testing.toml
index 7a278f3adac..09cd5061827 100644
--- a/scripts/config/notebook-testing.toml
+++ b/scripts/config/notebook-testing.toml
@@ -145,6 +145,10 @@ notebooks = [
   "docs/guides/function-template-hamiltonian-simulation.ipynb",
   "docs/guides/function-template-chemistry-workflow.ipynb",
 
+  # Deploys a Qiskit Function to Serverless and runs it, so it needs saved
+  # credentials and a deployed function. Not runnable in CI.
+  "docs/guides/function-template-aqc-trotter.ipynb",
+
   # Only works in runtime 0.41.1
   "docs/guides/monitor-job.ipynb",
 
@@ -222,6 +226,7 @@ notebooks = [
   "docs/tutorials/compilation-methods-for-hamiltonian-simulation-circuits.ipynb",
   "docs/tutorials/solve-market-split-problem-with-iskay-quantum-optimizer.ipynb",
   "docs/tutorials/simulate-neutron-scattering.ipynb",
+  "docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function.ipynb",
 
   # Don't test any learning notebooks
   "learning/courses/quantum-computing-in-practice/introduction.ipynb",

From 1140ea0679068de99c1e0afeb84cbd4ca79e4ef6 Mon Sep 17 00:00:00 2001
From: Parth Danve <parth.danve@uconn.edu>
Date: Tue, 11 Aug 2026 23:10:12 -0400
Subject: [PATCH 03/25] polish

---
 docs/guides/function-template-aqc-trotter.ipynb | 10 ++++------
 1 file changed, 4 insertions(+), 6 deletions(-)

diff --git a/docs/guides/function-template-aqc-trotter.ipynb b/docs/guides/function-template-aqc-trotter.ipynb
index 3f73a51b881..2a490e4867d 100644
--- a/docs/guides/function-template-aqc-trotter.ipynb
+++ b/docs/guides/function-template-aqc-trotter.ipynb
@@ -83,7 +83,7 @@
     "\n",
     "```\n",
     "your-working-directory/\n",
-    "├── deploy-aqc-dynamics-function.ipynb    <- this notebook\n",
+    "├── function-template-aqc-trotter.ipynb    <- this notebook\n",
     "└── source_files/                         <- the renamed folder\n",
     "    ├── __init__.py\n",
     "    ├── program.py\n",
@@ -480,8 +480,7 @@
     "        {\"n_steps\": 3, \"ansatz_steps\": 2},\n",
     "    ],\n",
     "    hamiltonian=H,\n",
-    "    backend=\"runtime\",\n",
-    "    backend_name=\"ibm_boston\",\n",
+    "    backend=\"runtime\"\n",
     ")\n",
     "print(\"job id (save this to reconnect later):\", job.job_id)"
    ]
@@ -540,7 +539,6 @@
     "# Run this only once the status cell above reports DONE. result() blocks until\n",
     "# the job finishes, so calling it earlier just waits.\n",
     "result = job.result()\n",
-    "print(\"execution backend:\", result[\"metadata\"][\"execution_backend\"])\n",
     "print(\n",
     "    \"AQC fidelities:\",\n",
     "    {k: round(v, 4) for k, v in result[\"metadata\"][\"aqc_fidelities\"].items()},\n",
@@ -569,7 +567,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "neutron-scattering",
    "language": "python",
    "name": "python3"
   },
@@ -583,7 +581,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3"
+   "version": "3.12.13"
   }
  },
  "nbformat": 4,

From 4194bac37fd7c57dee187e7db509e700614c5280 Mon Sep 17 00:00:00 2001
From: Parth Danve <parth.danve@uconn.edu>
Date: Wed, 12 Aug 2026 15:29:37 -0400
Subject: [PATCH 04/25] vocab

---
 .../function-template-aqc-trotter.ipynb       | 24 +++++++++----------
 docs/tutorials/_toc.json                      |  2 +-
 docs/tutorials/index.mdx                      |  2 +-
 ...on-scattering-with-a-qiskit-function.ipynb | 22 ++++++++---------
 4 files changed, 25 insertions(+), 25 deletions(-)

diff --git a/docs/guides/function-template-aqc-trotter.ipynb b/docs/guides/function-template-aqc-trotter.ipynb
index 2a490e4867d..a1292826f88 100644
--- a/docs/guides/function-template-aqc-trotter.ipynb
+++ b/docs/guides/function-template-aqc-trotter.ipynb
@@ -6,13 +6,13 @@
    "metadata": {},
    "source": [
     "---\n",
-    "title: Deploy and run the AQC-Trotter-dynamics Qiskit Function\n",
+    "title: Deploy and run a Qiskit Function template for AQC + Trotter Hamiltonian dynamics\n",
     "description: Deploy the AQC + Trotter Hamiltonian dynamics function template to IBM Quantum Serverless, then run it on a simulator and on a QPU.\n",
     "---\n",
     "\n",
     "{/* cspell:ignore Trotter Trotterization quimb cotengra cotengrust Suzuki fidelities isa */}\n",
     "\n",
-    "# Deploy and run the AQC-Trotter-dynamics Qiskit Function"
+    "# Deploy and run a Qiskit Function template for AQC + Trotter Hamiltonian dynamics"
    ]
   },
   {
@@ -32,7 +32,7 @@
    "source": [
     "## Overview\n",
     "\n",
-    "This is an experiment-agnostic Qiskit Function for Hamiltonian dynamics. Given a 1D nearest-neighbor Pauli Hamiltonian, a prepared initial state (optional), and a set of observables, it runs Trotter time-evolution, approximate quantum compilation (AQC) circuit compression, and mitigated execution, then returns each observable's time series. Swap the setup (PRE) and the analysis (POST) and the same core drives a different experiment:\n",
+    "This is an experiment-agnostic Qiskit Function template for Hamiltonian dynamics. Given a 1D nearest-neighbor Pauli Hamiltonian, a prepared initial state (optional), and a set of observables, it runs Trotter time-evolution, approximate quantum compilation (AQC) circuit compression, and mitigated execution, then returns each observable's time series. Swap the setup (PRE) and the analysis (POST) and the same core drives a different experiment:\n",
     "\n",
     "```\n",
     "   PRE (your setup)            FUNCTION (deployed here)             POST (your analysis)\n",
@@ -41,9 +41,9 @@
     " + optional local kick      -> <O>(t)                               quench dynamics, ...\n",
     "```\n",
     "\n",
-    "The template is published in the Qiskit function templates repository, alongside the other application templates. See: [AQC Dynamics Template](https://github.com/qiskit-community/qiskit-function-templates/tree/main/physics/aqc_trotter). This notebook deploys it to your own IBM Quantum&reg; Serverless account. Run it once, and any notebook can then call the function with `serverless.load(\"aqc-dynamics-function\")`.\n",
+    "The template is published in the Qiskit Function templates repository, alongside the other application templates. See: [AQC Dynamics Template](https://github.com/qiskit-community/qiskit-function-templates/tree/main/physics/aqc_trotter). This notebook deploys it to your own IBM Quantum&reg; Serverless account. Run it once, and any notebook can then call the function with `serverless.load(\"aqc-dynamics-function\")`.\n",
     "\n",
-    "For a worked scientific example, see [Simulate neutron scattering with an AQC-dynamics Qiskit Function](/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function), which calls this function to compute the dynamical structure factor of KCuF$_3$. This notebook covers deployment and the input contract instead."
+    "For a worked scientific example, see [Simulate neutron scattering with an AQC + Trotter dynamics Serverless workflow](/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function), which calls this function to compute the dynamical structure factor of KCuF$_3$. This notebook covers deployment and the input contract instead."
    ]
   },
   {
@@ -56,7 +56,7 @@
     "Before starting, be sure you have the following in this notebook's kernel environment:\n",
     "\n",
     "- Qiskit SDK v2.0 or later (`pip install qiskit`).\n",
-    "- The Qiskit IBM Catalog client (`pip install qiskit-ibm-catalog`), which uploads and runs Qiskit Functions.\n",
+    "- The Qiskit IBM Catalog client (`pip install qiskit-ibm-catalog`), which deploys and runs workloads on Qiskit Serverless.\n",
     "\n",
     "The function's own scientific dependencies (`qiskit-addon-aqc-tensor`, `cotengrust`, `qiskit-aer`) do not need to be installed locally."
    ]
@@ -68,7 +68,7 @@
    "source": [
     "## Get the template source files\n",
     "\n",
-    "The function is a small Python package that Qiskit Serverless runs in the cloud, so its source has to exist as local files that are uploaded at deploy time. The package is published in the Qiskit function templates repository.\n",
+    "The function is a small Python package that Qiskit Serverless runs in the cloud, so its source has to exist as local files that are uploaded at deploy time. The package is published in the Qiskit Function templates repository.\n",
     "\n",
     "Download **[`source_files`](https://download-directory.github.io/?url=https%3A%2F%2Fgithub.com%2Fqiskit-community%2Fqiskit-function-templates%2Ftree%2Fmain%2Fphysics%2Faqc_trotter%2Fsource_files)**\n",
     "\n",
@@ -480,7 +480,7 @@
     "        {\"n_steps\": 3, \"ansatz_steps\": 2},\n",
     "    ],\n",
     "    hamiltonian=H,\n",
-    "    backend=\"runtime\"\n",
+    "    backend=\"runtime\",\n",
     ")\n",
     "print(\"job id (save this to reconnect later):\", job.job_id)"
    ]
@@ -555,9 +555,9 @@
     "\n",
     "<Admonition type=\"tip\" title=\"Recommendations\">\n",
     "\n",
-    "- Work through [Simulate neutron scattering with an AQC-dynamics Qiskit Function](/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function), the companion example that calls this deployed function to compute the dynamical structure factor of KCuF$_3$.\n",
+    "- Work through [Simulate neutron scattering with an AQC + Trotter dynamics Serverless workflow](/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function), the companion example that calls this deployed function to compute the dynamical structure factor of KCuF$_3$.\n",
     "- Read the [AQC Dynamics Function Template Github](https://github.com/qiskit-community/qiskit-function-templates/blob/main/physics/aqc_trotter/) for the complete input and output contract, further examples, and citation details.\n",
-    "- Browse the [Qiskit function templates repository](https://github.com/qiskit-community/qiskit-function-templates/tree/main/physics/aqc_trotter) for other application templates built the same way.\n",
+    "- Browse the [Qiskit Function templates repository](https://github.com/qiskit-community/qiskit-function-templates/tree/main/physics/aqc_trotter) for other application templates built the same way.\n",
     "- Read the [Qiskit Serverless guide](/docs/guides/serverless) for managing deployed functions.\n",
     "- Go deeper on the AQC compression stage with the [Qiskit addon: AQC-Tensor](https://qiskit.github.io/qiskit-addon-aqc-tensor/) documentation.\n",
     "\n",
@@ -567,7 +567,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "neutron-scattering",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -581,7 +581,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.13"
+   "version": "3"
   }
  },
  "nbformat": 4,
diff --git a/docs/tutorials/_toc.json b/docs/tutorials/_toc.json
index 3787f0a0638..593f802891a 100644
--- a/docs/tutorials/_toc.json
+++ b/docs/tutorials/_toc.json
@@ -62,7 +62,7 @@
               "url": "/docs/tutorials/simulate-neutron-scattering"
             },
             {
-              "title": "Simulate neutron scattering with an AQC-dynamics Qiskit Function",
+              "title": "Simulate neutron scattering with an AQC + Trotter dynamics Serverless workflow",
               "url": "/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function"
             },
             {
diff --git a/docs/tutorials/index.mdx b/docs/tutorials/index.mdx
index 636e7bd2968..745c01f55e4 100644
--- a/docs/tutorials/index.mdx
+++ b/docs/tutorials/index.mdx
@@ -51,7 +51,7 @@ These tutorials focus on estimating physically meaningful quantities, such as en
 
 * [Simulate neutron scattering in quantum materials with quantum circuits](/docs/tutorials/simulate-neutron-scattering)
 
-* [Simulate neutron scattering with an AQC-dynamics Qiskit Function](/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function)
+* [Simulate neutron scattering with an AQC + Trotter dynamics Serverless workflow](/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function)
 
 * [Krylov quantum diagonalization of lattice Hamiltonians](/docs/tutorials/krylov-quantum-diagonalization)
 
diff --git a/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function.ipynb b/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function.ipynb
index 5430b8f60b0..50e1a675ad1 100644
--- a/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function.ipynb
+++ b/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function.ipynb
@@ -6,13 +6,13 @@
    "metadata": {},
    "source": [
     "---\n",
-    "title: Simulate neutron scattering with an AQC-dynamics Qiskit Function\n",
-    "description: Compute the dynamical structure factor S(q, w) of the quantum magnet KCuF3 by running an AQC-compressed Trotter workflow as a Qiskit Function.\n",
+    "title: Simulate neutron scattering with an AQC + Trotter dynamics Serverless workflow\n",
+    "description: Compute the dynamical structure factor S(q, w) of the quantum magnet KCuF3 by running a Trotter workflow with AQC compression as a deployed function template.\n",
     "---\n",
     "\n",
     "{/* cspell:ignore Trotter Trotterization spinon spinons KCuF quimb DMRG magnon antiferromagnetic Suzuki fidelities isa COBYQA Gjjc qpoints viridis fontsize vmax vmin */}\n",
     "\n",
-    "# Simulate neutron scattering with an AQC-dynamics Qiskit Function\n",
+    "# Simulate neutron scattering with an AQC + Trotter dynamics Serverless workflow\n",
     "*Usage estimate: 18 minutes on a Heron r3 processor (NOTE: This is an estimate only. Your runtime might vary.)*"
    ]
   },
@@ -27,7 +27,7 @@
     "\n",
     "- How an inelastic neutron-scattering spectrum maps to the dynamical structure factor $S(q, \\omega)$ of a 1D quantum magnet.\n",
     "- How to prepare the KCuF$_3$ (isotropic Heisenberg) ground state with the density matrix renormalization group (DMRG) and matrix product state (MPS) fidelity maximization.\n",
-    "- How to run Trotter time-evolution, approximate quantum compilation (AQC) circuit compression, and mitigated execution as a single Qiskit Function call.\n",
+    "- How to run Trotter time-evolution, approximate quantum compilation (AQC) circuit compression, and mitigated execution as a single function call.\n",
     "- How to post-process the per-site $\\langle \\sigma_z \\rangle(t)$ time series into $S(q, \\omega)$ and identify the two-spinon continuum."
    ]
   },
@@ -70,7 +70,7 @@
     "\n",
     "So the experiment-specific work stays here in the notebook: ground-state preparation (PRE) and the $S(q, \\omega)$ post-processing (POST). The two quantum-heavy steps, compression and execution, run inside the function.\n",
     "\n",
-    "This tutorial is a companion to [Simulate neutron scattering in quantum materials with quantum circuits](/docs/tutorials/simulate-neutron-scattering), which builds the same experiment inline: the same KCuF$_3$ model, ground-state preparation, neutron kick, and post-processing, with the Trotter synthesis, AQC compression, and mitigated execution written out step by step. Read that tutorial to learn how AQC compression works. Read this one to run the same experiment through a deployed Qiskit Function: the quantum core becomes a single function call, and the multi-hour AQC compression runs inside the Serverless worker instead of on your machine, so you do not need an HPC system or an open kernel while it runs. Because the function is Hamiltonian-agnostic, the same call also drives other dynamics experiments."
+    "This tutorial is a companion to [Simulate neutron scattering in quantum materials with quantum circuits](/docs/tutorials/simulate-neutron-scattering), which builds the same experiment inline: the same KCuF$_3$ model, ground-state preparation, neutron kick, and post-processing, with the Trotter synthesis, AQC compression, and mitigated execution written out step by step. Read that tutorial to learn how AQC compression works. Read this one to run the same experiment through a deployed function template: the quantum core becomes a single function call, and the multi-hour AQC compression runs inside the Serverless worker instead of on your machine, so you do not need an HPC system or an open kernel while it runs. Because the function is Hamiltonian-agnostic, the same call also drives other dynamics experiments."
    ]
   },
   {
@@ -82,7 +82,7 @@
     "\n",
     "Before starting this tutorial, be sure you have the following:\n",
     "\n",
-    "- The function deployed to your IBM Quantum&reg; Serverless account. Run the companion function template first: [Deploy and run the AQC dynamics Qiskit Function](/docs/guides/function-template-aqc-trotter). That guide walks through getting the source files and uploading the function to your account. This tutorial only calls the deployed function.\n",
+    "- The function deployed to your IBM Quantum&reg; Serverless account. Run the companion function template first: [Deploy and run the AQC + Trotter dynamics function template](/docs/guides/function-template-aqc-trotter). That guide walks through getting the source files and uploading the function to your account. This tutorial only calls the deployed function.\n",
     "\n",
     "- IBM Quantum credentials saved for `QiskitServerless` (see the function template). Both examples below call the deployed function, so both need them.\n",
     "\n",
@@ -344,7 +344,7 @@
    "id": "setup-load-md",
    "metadata": {},
    "source": [
-    "### Load the Qiskit Function\n",
+    "### Load the function template\n",
     "\n",
     "Connect to IBM Quantum Serverless and load the deployed `aqc-dynamics-function`. Both examples below call the same `fn` handle, so the function is loaded once, here."
    ]
@@ -431,9 +431,9 @@
    "id": "small-s3-md",
    "metadata": {},
    "source": [
-    "### Steps 2 and 3: Compress and execute with the Qiskit Function\n",
+    "### Steps 2 and 3: Compress and execute with the function template\n",
     "\n",
-    "In a hand-written workflow these are two separate stages: optimize the circuits for hardware (Step 2) and execute them (Step 3). The Qiskit Function collapses both into one call. It performs Trotter synthesis, AQC compression, and hardware transpilation, then runs the circuits (here on the exact simulator, later with built-in error mitigation on hardware). The two tuning parameters are `aqc_segments` (the compression plan) and `aqc_options` (the MPS and optimizer settings). Each segment `{\"n_steps\": k, \"ansatz_steps\": m}` compresses `k` consecutive Trotter steps into an ansatz built from an `m`-step Trotter target, and any steps beyond `sum(n_steps)` run as plain Trotter. Early, low-entanglement steps compress well into a shallow (`ansatz_steps=1`) ansatz, so here we compress the first 3 steps into a 1-layer ansatz and the next 2 into a deeper 2-layer ansatz; the remaining 5 of the 10 Trotter steps run as plain Trotter. For `aqc_options` we mirror the original tutorial: MPS bond dimension `max_bond=32`, `cutoff=1e-8`, and an L-BFGS-B optimizer capped at 100 iterations.\n",
+    "In a hand-written workflow these are two separate stages: optimize the circuits for hardware (Step 2) and execute them (Step 3). The function template collapses both into one call. It performs Trotter synthesis, AQC compression, and hardware transpilation, then runs the circuits (here on the exact simulator, later with built-in error mitigation on hardware). The two tuning parameters are `aqc_segments` (the compression plan) and `aqc_options` (the MPS and optimizer settings). Each segment `{\"n_steps\": k, \"ansatz_steps\": m}` compresses `k` consecutive Trotter steps into an ansatz built from an `m`-step Trotter target, and any steps beyond `sum(n_steps)` run as plain Trotter. Early, low-entanglement steps compress well into a shallow (`ansatz_steps=1`) ansatz, so here we compress the first 3 steps into a 1-layer ansatz and the next 2 into a deeper 2-layer ansatz; the remaining 5 of the 10 Trotter steps run as plain Trotter. For `aqc_options` we mirror the original tutorial: MPS bond dimension `max_bond=32`, `cutoff=1e-8`, and an L-BFGS-B optimizer capped at 100 iterations.\n",
     "\n",
     "Call the function loaded in Setup. `backend=\"statevector\"` runs the exact reference path: no QPU time, with the circuits running on an exact statevector simulator inside the serverless worker (a saved Serverless account is still needed to call it). The `initial_state` carries the prepared ground state (including the kick); `observables` is omitted so the function measures the default per-site $Z$."
    ]
@@ -658,7 +658,7 @@
    "id": "large-s3-md",
    "metadata": {},
    "source": [
-    "### Steps 2 and 3: Compress and execute with the Qiskit Function\n",
+    "### Steps 2 and 3: Compress and execute with the function template\n",
     "\n",
     "The same single call as the simulator example, now with `backend_name` pointing at a real IBM Quantum processor, so the function transpiles and executes there. The compression plan varies the ansatz depth: the first 6 (low-entanglement) Trotter steps compress into a shallow 1-layer ansatz, the next 4 into a deeper 2-layer ansatz, and the remaining 10 of the 20 steps run as plain Trotter. `aqc_options` raises the MPS bond dimension to `max_bond=128` for the longer, more-entangled chain (matching the original), keeping the same L-BFGS-B optimizer capped at 100 iterations. The `estimator_options` turn on the built-in error mitigation: dynamical decoupling (XY4), gate twirling, and TREX measurement mitigation. The function's defaults already match the original tutorial for all of these except the TREX learning budget (`measure_noise_learning`), which is the only genuine difference. The whole block is still written out because a caller-supplied `estimator_options` replaces the function's defaults wholesale instead of merging into them, so omitting a key would fall back to the Qiskit Runtime default rather than the function's."
    ]
@@ -880,7 +880,7 @@
     "\n",
     "- Adapt this workflow to your own system: the function is Hamiltonian-agnostic, so a different `SparsePauliOp`, initial state, or set of observables runs the same PRE &rarr; FUNCTION &rarr; POST pipeline. See the full input/output contract in the [AQC Dynamics Template](https://github.com/qiskit-community/qiskit-function-templates/tree/main/physics/aqc_trotter).\n",
     "- Read the paper this benchmark comes from: Lee et al., [*Benchmarking quantum simulation with neutron-scattering experiments*](https://arxiv.org/abs/2603.15608) (arXiv:2603.15608).\n",
-    "- Compare with the [original \"Simulate neutron scattering\" tutorial](/docs/tutorials/simulate-neutron-scattering), the inline workflow this one ports onto a Qiskit Function.\n",
+    "- Compare with the [original \"Simulate neutron scattering\" tutorial](/docs/tutorials/simulate-neutron-scattering), the inline workflow this one ports onto a deployed function template.\n",
     "- Go deeper on the [error mitigation and suppression techniques](/docs/guides/error-mitigation-and-suppression-techniques) applied on the hardware run: dynamical decoupling, Pauli twirling, and TREX.\n",
     "\n",
     "</Admonition>"

From b2a24f12cbf95c36b9a57b59b56fcf5734ad48df Mon Sep 17 00:00:00 2001
From: Parth Danve <parth.danve@uconn.edu>
Date: Wed, 12 Aug 2026 16:34:18 -0400
Subject: [PATCH 05/25] rename+appendix

---
 .../function-template-aqc-trotter.ipynb       |   4 +-
 docs/tutorials/_toc.json                      |   2 +-
 docs/tutorials/index.mdx                      |   2 +-
 ...attering-with-a-serverless-workflow.ipynb} |  36 ++++++++++++++++--
 .../appendix-dsf-10.avif                      | Bin 0 -> 14465 bytes
 .../appendix-dsf-20.avif                      | Bin 0 -> 16574 bytes
 .../appendix-dsf-30.avif                      | Bin 0 -> 16131 bytes
 .../extracted-outputs/large-result-0.avif     | Bin
 .../extracted-outputs/large-result-1.avif     | Bin
 .../extracted-outputs/small-s4-code-0.avif    | Bin
 .../extracted-outputs/small-s4-code-1.avif    | Bin
 qiskit_bot.yaml                               |   2 +-
 scripts/config/notebook-testing.toml          |   2 +-
 13 files changed, 38 insertions(+), 10 deletions(-)
 rename docs/tutorials/{simulate-neutron-scattering-with-a-qiskit-function.ipynb => simulate-neutron-scattering-with-a-serverless-workflow.ipynb} (92%)
 create mode 100644 public/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/appendix-dsf-10.avif
 create mode 100644 public/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/appendix-dsf-20.avif
 create mode 100644 public/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/appendix-dsf-30.avif
 rename public/docs/images/tutorials/{simulate-neutron-scattering-with-a-qiskit-function => simulate-neutron-scattering-with-a-serverless-workflow}/extracted-outputs/large-result-0.avif (100%)
 rename public/docs/images/tutorials/{simulate-neutron-scattering-with-a-qiskit-function => simulate-neutron-scattering-with-a-serverless-workflow}/extracted-outputs/large-result-1.avif (100%)
 rename public/docs/images/tutorials/{simulate-neutron-scattering-with-a-qiskit-function => simulate-neutron-scattering-with-a-serverless-workflow}/extracted-outputs/small-s4-code-0.avif (100%)
 rename public/docs/images/tutorials/{simulate-neutron-scattering-with-a-qiskit-function => simulate-neutron-scattering-with-a-serverless-workflow}/extracted-outputs/small-s4-code-1.avif (100%)

diff --git a/docs/guides/function-template-aqc-trotter.ipynb b/docs/guides/function-template-aqc-trotter.ipynb
index a1292826f88..c1d7ba0d496 100644
--- a/docs/guides/function-template-aqc-trotter.ipynb
+++ b/docs/guides/function-template-aqc-trotter.ipynb
@@ -43,7 +43,7 @@
     "\n",
     "The template is published in the Qiskit Function templates repository, alongside the other application templates. See: [AQC Dynamics Template](https://github.com/qiskit-community/qiskit-function-templates/tree/main/physics/aqc_trotter). This notebook deploys it to your own IBM Quantum&reg; Serverless account. Run it once, and any notebook can then call the function with `serverless.load(\"aqc-dynamics-function\")`.\n",
     "\n",
-    "For a worked scientific example, see [Simulate neutron scattering with an AQC + Trotter dynamics Serverless workflow](/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function), which calls this function to compute the dynamical structure factor of KCuF$_3$. This notebook covers deployment and the input contract instead."
+    "For a worked scientific example, see [Simulate neutron scattering with an AQC + Trotter dynamics Serverless workflow](/docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow), which calls this function to compute the dynamical structure factor of KCuF$_3$. This notebook covers deployment and the input contract instead."
    ]
   },
   {
@@ -555,7 +555,7 @@
     "\n",
     "<Admonition type=\"tip\" title=\"Recommendations\">\n",
     "\n",
-    "- Work through [Simulate neutron scattering with an AQC + Trotter dynamics Serverless workflow](/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function), the companion example that calls this deployed function to compute the dynamical structure factor of KCuF$_3$.\n",
+    "- Work through [Simulate neutron scattering with an AQC + Trotter dynamics Serverless workflow](/docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow), the companion example that calls this deployed function to compute the dynamical structure factor of KCuF$_3$.\n",
     "- Read the [AQC Dynamics Function Template Github](https://github.com/qiskit-community/qiskit-function-templates/blob/main/physics/aqc_trotter/) for the complete input and output contract, further examples, and citation details.\n",
     "- Browse the [Qiskit Function templates repository](https://github.com/qiskit-community/qiskit-function-templates/tree/main/physics/aqc_trotter) for other application templates built the same way.\n",
     "- Read the [Qiskit Serverless guide](/docs/guides/serverless) for managing deployed functions.\n",
diff --git a/docs/tutorials/_toc.json b/docs/tutorials/_toc.json
index 593f802891a..be43d1d77fc 100644
--- a/docs/tutorials/_toc.json
+++ b/docs/tutorials/_toc.json
@@ -63,7 +63,7 @@
             },
             {
               "title": "Simulate neutron scattering with an AQC + Trotter dynamics Serverless workflow",
-              "url": "/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function"
+              "url": "/docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow"
             },
             {
               "title": "Krylov quantum diagonalization of lattice Hamiltonians",
diff --git a/docs/tutorials/index.mdx b/docs/tutorials/index.mdx
index 745c01f55e4..b9e9b052187 100644
--- a/docs/tutorials/index.mdx
+++ b/docs/tutorials/index.mdx
@@ -51,7 +51,7 @@ These tutorials focus on estimating physically meaningful quantities, such as en
 
 * [Simulate neutron scattering in quantum materials with quantum circuits](/docs/tutorials/simulate-neutron-scattering)
 
-* [Simulate neutron scattering with an AQC + Trotter dynamics Serverless workflow](/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function)
+* [Simulate neutron scattering with an AQC + Trotter dynamics Serverless workflow](/docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow)
 
 * [Krylov quantum diagonalization of lattice Hamiltonians](/docs/tutorials/krylov-quantum-diagonalization)
 
diff --git a/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function.ipynb b/docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow.ipynb
similarity index 92%
rename from docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function.ipynb
rename to docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow.ipynb
index 50e1a675ad1..8f6591144ce 100644
--- a/docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function.ipynb
+++ b/docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow.ipynb
@@ -541,7 +541,7 @@
     {
      "data": {
       "text/plain": [
-       "<Image src=\"/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/small-s4-code-0.avif\" alt=\"Output of the previous code cell\" />"
+       "<Image src=\"/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/extracted-outputs/small-s4-code-0.avif\" alt=\"Output of the previous code cell\" />"
       ]
      },
      "metadata": {},
@@ -550,7 +550,7 @@
     {
      "data": {
       "text/plain": [
-       "<Image src=\"/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/small-s4-code-1.avif\" alt=\"Output of the previous code cell\" />"
+       "<Image src=\"/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/extracted-outputs/small-s4-code-1.avif\" alt=\"Output of the previous code cell\" />"
       ]
      },
      "metadata": {},
@@ -828,7 +828,7 @@
     {
      "data": {
       "text/plain": [
-       "<Image src=\"/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/large-result-0.avif\" alt=\"Output of the previous code cell\" />"
+       "<Image src=\"/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/extracted-outputs/large-result-0.avif\" alt=\"Output of the previous code cell\" />"
       ]
      },
      "metadata": {},
@@ -837,7 +837,7 @@
     {
      "data": {
       "text/plain": [
-       "<Image src=\"/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/large-result-1.avif\" alt=\"Output of the previous code cell\" />"
+       "<Image src=\"/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/extracted-outputs/large-result-1.avif\" alt=\"Output of the previous code cell\" />"
       ]
      },
      "metadata": {},
@@ -869,6 +869,34 @@
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "appendix-md",
+   "metadata": {},
+   "source": [
+    "## Appendix: How the workflow scales\n",
+    "\n",
+    "The hardware example above runs a single chain length. The three spectra below come from earlier hardware runs of this same workflow on `ibm_pittsburgh` at 10, 20, and 30 sites, with every other input held fixed: 20 Trotter steps at `dt = 0.6`, the compression plan of 6 one-layer plus 4 two-layer segments, and `max_bond = 128`. These are recorded results, not output from the cells above.\n",
+    "\n",
+    "![Dynamical structure factor at 10 sites, a single sharp bright peak at q = pi near the lower bound](/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/appendix-dsf-10.avif \"10 qubits\")\n",
+    "\n",
+    "![Dynamical structure factor at 20 sites, spectral weight filling the band between the two dashed two-spinon bounds](/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/appendix-dsf-20.avif \"20 qubits\")\n",
+    "\n",
+    "![Dynamical structure factor at 30 sites, the continuum resolved more finely with fainter contrast and some weight outside the bounds](/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/appendix-dsf-30.avif \"30 qubits\")\n",
+    "\n",
+    "All three recover the two-spinon continuum, brightest at $q = \\pi$ and bounded by the dashed curves, so the physics holds at every size. What changes with chain length is a tradeoff rather than a straight improvement. Momentum resolution sharpens as $\\Delta q = 2\\pi / n$, so 30 sites map the shape of the continuum far more finely than 10 can. Signal quality moves the other way: longer chains mean deeper circuits, so noise accumulates, contrast fades, and spurious weight leaks outside the bounds.\n",
+    "\n",
+    "The two halves of the workflow scale differently in cost as well:\n",
+    "\n",
+    "| Qubits | Classical (build + AQC) | QPU usage |\n",
+    "|---|---|---|\n",
+    "| 10 | 4m 3s | 14m 21s |\n",
+    "| 20 | 24m 52s | 15m 58s |\n",
+    "| 30 | 230m 57s (about 3h 51m) | 17m 39s |\n",
+    "\n",
+    "Queue time is not counted in either column. The classical stage climbs steeply, roughly 6 times from 10 to 20 qubits and another 9 times to 30, dominated by the AQC fidelity optimization at `max_bond = 128`. QPU usage grows only about 1.2 times across the same range, because the circuit count and shot budget follow `t_steps` and the twirling settings rather than the qubit count. At 30 qubits the classical stage costs roughly 13 times the QPU time, which is the practical argument for rehearsing on `statevector` or `fake` before committing to hardware."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "nextsteps",
diff --git a/public/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/appendix-dsf-10.avif b/public/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/appendix-dsf-10.avif
new file mode 100644
index 0000000000000000000000000000000000000000..66e03e792b3476487a62d867a77cb6115e7c1397
GIT binary patch
literal 14465
zcmZ9zV~{9I6D>Nn_t^Fx+uCE>wr$(CZQHhO+cxh$=e+OTh+7fexpHM@RagG$sHj)~
z007v=j&8Pk&Su5{e}|QsG5tT-%1rOC4_IVn<f!)#|923W8d}=_2LS+V%?up>hyQ1V
z%`9yU{&9r(0Du7i|3QFb|5*S47IFZfe;C%g|1kgpf3yA$Hpc&-k^bvSnVDJt`zilM
zQT{qc{}%q8PtTc-{-28fOa6`gza-#4b$?v{+YX$qnWNP|CI2o_V`gh_`xl3s*&5jV
zwJ>_l^n!jMF#p7n%^Ylv{s900Ko5V@&;J4hTQgU)e;8m02#CMt|I5ezhx$A7e=)#+
zB5Zn2u7dy2PWILuR<=ea|Goq`49pB{IUL*^9F43TIR56CS(@qDyK(4Q+M52`Rva^1
ztN-;E2mlb+4+t0l2qX{)H2Cja<Yx9p|8?T;a(|00|8@}Y-;KZ-7&;jI+vC4oVd=Fp
z)N@1vfP%sxrKOb+0%5{LU`Vl)40UzY$DEmj0n}odNRl>KK;ydw>PAw&O#bEAX1C&<
zWfs}WUcjINb4Qj}jJ+J)ZRVYR87x)a>%#%`W56B#Z2e-`CEDTWvGwtUf2_5Jbz(zW
zQ6wP3wFM(4@A8JX!OIx}xtX1!bvmR3zo(g$?d}NnB_Zu}dPz|07QwR<To2$CpSrkd
z*#Fh5>s9~ZEb?T2KF?N(<)>_|q)Y}FJ2HxE^fk#vlcyB-x`z-ZBt#rCU9I1`^FD}+
zMKIH#Ub^>^S@-DF(fRg;-|jNKZM~})Xn5q(aZ()V(!(ti3c~)_?x3ViJbi9zud-@5
z(P6E-Xq6Vs;J0G1y^0(N!YJ~SQe^M`f{cJh9GJ5_0_&^LD;2F4F2PDS=h$`}3|*9+
z=?2idZ9jWhYz#Rn0y5u+(z}!lPdK4t%ytg(B!3cpIJ*2<UI=XJ#OWci#}=~~MvV52
zzp+UW&nOr*q_-xCHK~P(ZEqHcrscltc(7fKcu~K$w{8}Ksf(6d!A{05pKMCJR5xgv
z1O%x|_TuwXfZ&|!qX^azUl{Ifbn8xNieay-9pEu>mSH(Ty12XyCA=8e_(=`Wq3!?+
z5$+II(qV6dd$@~hw5}~QDOYQ=Nbkgd>|>IZ9jZiABWx_03l*pwna~t+7`8D|MHPNX
z4g=_lq0*HXnFRo~>E#*vTmLkwjB{k40yb{`HdZ<5yUvovd@slH+s<*7?p117z}383
zES}e^HJa^M0x-s;7i9b4!N-h&&>z$6W^8jY%lkCyJ?q`B+_VKA5&jf~yf2`tHIQgy
z#gDuuP-8&YmN|2}@s0?M4&uUFrbr1GrtN1I)<bE!M%$xWobKU$vJRf_!9|9kW5I@H
z>d#yM++zIByeJ!`XQIsGW%sv2w|DesHI^&lrcT^BNZ$^6PDflMkY~Omz*GW<*uKQC
zloACiOX(^H?$~Dc_41awSK-=>jL>U)vXfYL)=#%&^&WOuaQYO=EF<p>m4c@R=~kUY
zgYk>2GZOB@Z_XjJQA*uo1~&c_@`N(E*EgQ^S!=!90Q(1EyKsFAguKbKVRqoX-+gV>
z2+_Ss<0#VT@N+yEqK*r#J)MtvU*4IsPiR*rQ2h>m5K#KQ4tA9|ryBfM)7F;6gnKu<
zo;L9_Fv9^8f%>qO4qWD13}$s!9#|Lah)a^jaB=YZxN~5uSIVD13aZus#yLuET)m#i
zD1bGczA~rw;KiMIGUkjH4T_fbZy`65v3xI;O54|p5L^Atb4wqb?dafz(22E;u+9tG
zKcK;YfCTyVmLxuiLC;ctLGq=nIMhL!#xQ~{pe_w;9?tX5VzJOS1~lB&=x{3qLmGCG
zh92&hynUINpRbC+^E5Rfk_eJ}p#kh${%c#h!q;!M5u%NPMsQy4kDt?e@Op^ep;%h)
zSe@u1W%1PtgBxK=HBhb~-hUV*`uXK&<rM?3mY0ZKC0RCZk5>l3H%Xhx0&Ks0<+8Kq
zcEqt_Tj6C)O)8NUWLZFiX!nS8b@-T2ybYIi^TxG^J+{h3F8I*zyBH!^I`9P#i0Ii9
zAP!d&6?xA<XFzIP;%YR%;`yz?h(|ws6mvKxA3{oH8IF~n{fJM)ly8dKhf0eW^a7+w
z-X|PA3dnaS1@XM`QE}Gs;C?sR%$z#o1$|IR23p|>CpBxVJo?7CZz=T^O=m6ad)zb5
zqCxZK!%O%?5SD<U;hrC9zn88GI(d=rq-vgkI6=E*l%hsYgyU-8JI#*^mc%I4CwjU%
z*Ujk*DFA{X5=8?S>Z+;s1XHWV6;S`Q<n8EZCjMdX%tKYNlV`Zdekp|ud=Z`5#&BcB
zS*h0h0xn03G{}u*xq_5wQI^k1ZX9mU{#{Ci9WGnnRo!Ihy%fXKRf^zv!c^oxBzK=`
z&kCVCmd&aQ=YF}IDmbym?B-dCH*YbU!W1=?$lR0<Y6jQ_*j|&Y|0+#Ew}JXOHQq&n
zj|76>VqkCsL+2lO-=+bFFq=*!$PpehjH98Cj3arL7Po&`c2_g+ig3Ev4+W%yo<f7J
z*iH-v51yi%J>du;?tJu-AdPEP{85k?Ls^#SPN@Mj3-BC~wOq`2ZC(qmOKjF~)rGu6
z>GeX*27<(=GPXUZx5Lbn8oXrhD7T~h7MT&qSYp{#sWuP)8O8_aD747?@{XPAb4k$;
z78~!eA(0RC+3i+-Fn>sO5=o8tOc-%8DcI+pKxaSMaS|+k4Z6kAaS=*Cb+9}Kc`K$c
zM%v4I2*1gIu)!Za_4wy~sO)JHd}dfEofDMc!sQi_)GvQNUy1tgOu5Q=OXq}vnL~?5
zTfg`PgS?`S;Ma+9E_W1#i2RJ44oFqcgP2W}e;C!z0Gz~dp}Vo2#Yk#}Ol>Tij@`YZ
zR-YUs<dFo};rM$ebhVuuY!*AQ??8CDqX=LXIttcSj>M_(O|>{40*De#o}0J?8X>L*
zLhIGjHZ~CUXgh<4&qA@ySVIK>egNtYw=dc{SFLOs0KJ&J6IdREDoTn(Y859}Ty#4#
zuTgZ4K71#dQ|Se$)5{)=`v~np@S%voGS43<k=E<e7Y-;ElqhJofpGECL-wS6bzOsL
zNSZ2K!II2b>aRYAu5hExvaodMjjeaPo<QHojN}|1zDjqpWWcIJ>PO_|Cp$3THwq8@
zZ`nVW8k^-jJL3L~C|tNH%>Jsyc>xYfwiAnYHry*%VdMZocpgE;CnVWQ<7l(^^si@n
zVCe+VZqN9|$1=`{-4pC#T)hW;bL;W%2KjbCtuKo*ZLYUKJ6p~GAg64@vw3)Uu@!tI
z*kfzdeZa6WewRHR+kriRJy%ft0UuJN1X@60eEQJ%ILfJ4&5e`p-V260t;^WHaJGjP
zw(+!|r$sn74Zx0z8`2XIXmcB9C~N}6mX?vY+NZ64=gQ~$dT#zyOG|l>H^o|4_|aAH
z3M3!uDOlZ3c?VHe5N0-_2o7KUm@sI|6hD;3dAT!VMOV)}K|)FnrTSaW!rTu%DuXxl
z40AxFRDM?;A}2+CbWPTRMIq(ca~HS$j|?ZWy}cGDA5tD^zjQzDGxrinmqD1!wh!(5
zu&>Ridl2!W%*SUnVtT=lHFPUpTk+yshKUfHsla}tSKDagbu!eSG!s&HAZ6bnS=^B$
zujAiUD_OcYZa*xT0!~b|cGuaV(s$zHY#V|wVnJ>hSyClXYogUPVk!NSJ}n$IJF{Xt
zfW_myaz0MHWeWb?ecO?M5h_Re>Fuwuv@6eT*@T0gzZ>@@{7ocs1chU+L_@hD*S<AJ
zl{sr~3JFxTnrXd|eV|z2&Y&odatxFkTXM7N@^~U@1D+d>2*p(9sCtST2SHviqCO1W
z2dRDbQ-c}e(bqjJ3NC2fp@UqTc2KZJQY2CaJxe1kabMgw9eTavw($d11@GD{f{=JC
zB{ly%SZI;foVBVLHA{6ue#Pl#<<6jTUEYQC;juzm{w9U!*h3j%jI;Mm%+^3h8_1oU
zS;f!+-eCEjkPnSPfA|5|oZr#xY#4srKuX&;61>(VibyTKzfteiy(}3L=Yz5q0$MW8
zhRll824A6!OfwmtHqUPcUd6j~ky4`TP*MNc@@U1lP6E1xl~5e`ey}zY!Vx&Y0~nQ;
z%~^BaM3S7VTW^!TrCN{$j<tRLeb9R7b%NnLKem+4f65;HL64g;l1pUAw`}MBmJ!ZP
z`@1tA-M3vP>9JAolUr=MeM0X+n5@f4mn4Bde~2YcJtVr`-@y<Yrxc<LaaAjzX6ey}
z;>PqUMN=5A_(hNvRAs3p5Kr-U0agh8xdDRELA6xZg9+&+)p{~;)l<0VAGHG+CEojd
zC)wvBf@M7%_}1o>2Fp}-vd=`&3qam>Wg%rE1NGK{2s%H78tWym!pFyTWBHEXg?@M_
z=jY|wCntp-b*r$0hVVUKM;0{0f!9z<H~MDFam5e|M3lY}R{WGOdqi4Hpk!_A92{~_
z8xC*mArYs{x0mX$o+Tyx^eq*Rcg0!#Ci;Cy9aQD0R#6V`DC;>kX$LdjzY-7nN6j{m
z5tPX2r$4E>aW^-i4k)u-KA~md-d#~Y4?ujxs2%+rMJsPIff{Uuyq*&xRI?=fVq7WN
zQi=rTAD5yh=3>mR1yfecb+6;Os0s3V%pWkj+QDcyWV`$bl8CI6P*|U8*S#s2<RVtD
zBqX_woMw)cO;FBbJ6dE0=`Nb!<@e1=aan*5gj5T`XK~fH3We_OqNNtI+$sLpTg3fx
z8cM5My*%XPcM8w!;qdO#B%!-J*Ff9O1n)U-y>l5xAE@0?;Pw#92e0J;E^O26Vrfa=
zJmQL8XN?Y1H6C!xkvh*t41=s2>806|&FL2#9b)g0VNAa=Neia~p#Ke#<W_5fYjEDL
z7VLfxmPoblrhwfpFO>VK8av4sejUS9XB0<k+gts%!X(ld2@o`=NHsBxP~(OSAB->;
z?E2}iYGDMmA4>zB>ZkrT^#upn5nXYnO=;cwv{3%IZDl$%1V`y-2W9cjOPl32hZRa4
zVHw4kZhV+aonHPd>MBQ8uL_YtjH?whQtzjeNhaVXF5YVNjT{DGd_Myy?A-_4(8~pH
z=2Wk~tx&xwi@Q~fNUd5*PE;_MY@=0}rXfiXGtdip{G@T=7&DR;8Qru_ew&f2sWmbJ
zUis~Z)*ZHlMacvygcu@vs%i~W0lx?c$xQ$(D!W?;STB}fnK#8?obE9)_Mno#mC5S?
z#2#W-MXunn*>n1x8-Aw2G#$c{YJlDH3XcX4SPEGUzpe&En8XgCN=IziM=~K3CBHgR
zX}Gubo|ld!c<IZqanX3#nA@Y(ea=Nnoud+-Mp*JnD9u=b!-Q~eE<flJ^tICN;Q+=)
z&a>Q=wpH;VtF6;<7Z|>TqZ*RsFKawKbnHTd=6i8m0>kBTK;QHC)KXCsWTY!oOdd<p
zL2l#zlDj0OFUSe{p$9^v`8p}35;gjfECW!P^u{^R?#nHH0%FT9`DO151sMTeq+THY
zF^{#Y=?83;I1^9Kqh3m|AFE~yIpM4&QOY3~%{%;p(a_G9aBi;$;M?f^KK8N#xix=I
zLDi-Sq}h6%LAWxfUGA|SMYn*^`*Stcte6wWsFz-_QJX4Q=Wgy<IO<5to*6tXAYgTP
z7iIy&)CReCsYG~FA9#G3C;^8=ovAf5yXq;WQv8z;G@`rPyMy|2Uo>$B8>U1k^e3Ak
z*!qOYH41#~)Dcvt-<WsB{a%~MfpPG3`&QsdsOkI~xDSBB6Kio4D$BH?e>kRChmxNx
zVbbSYs--K~76)?Qgsbitfp8*lB@`+djr$F_O)`%nWyGgb-^-vTV7M^~-9QTnui!eq
z4om%mURI#1eaJZZ0gG5<+vQGsf&;q#H>LD7{Qgdd_D&#|F-Ljvgj<L$Q!e%vg60mc
z^rJ@XJ>5ug@YO!8l<@vMnZkZ_jhD3J78EpeNSypBMJ(jz1rgP14#tGmOkPx9Up3jN
z+v$<)5b(yU`z5Hmbu)pRM)?>k4jlRxaJpF8%b)?FG!BV@lZcraIyv2Jp9Dp=)G8#F
z-dg6U(ayF}!MpDr-t7t(uqfy3qdLbgeF*+Aa>=X3so1dQtoCxxy<Z|MDLL`rOq%DF
z>#FCmGh(vJA>ws){b3#2Y8f<ldY-?T7I^Zr3?;kkPQu20P34xndG*%ne(JKod4;`(
z2h@KoVKk1&K3yTAfGVRfieCo=qLZkG^vOd>Vs0>SkEf?RkTD$ZN=M8f6l~X&RmEQ!
zOu(V1gRJA3y1easkU1x4GRrCtKYkkr5UNUS)<7`p;~8KxS7)h;VKw`wcQ|k}<`<9n
zdN4xWPOvr2oTa1F<2&-eG20EEBDMwhPpfW}aWkPGPq@F<q3OgR#z=6F4WlBei|+%0
z3le)Y8C_zhjg5pmlqLu1y)LwJ&%pB!Ni)NpcqX^r0e`O)o3keRKBAorhbqGQ7J&Dw
zkiV!!8a@4zf7}6h`U651K_cnhO<A;Oe?UO^qwfdK(g{hJy$gJ=B+dGponqLzN)0S)
zFW*SxXviiC*K_HyU4?GI?>zP42L&M+1IyTIR-+k^If{<nDkUK0CsjM-`6*vj4%?C*
zmt&Q0AJsKbioGs7Z@Kvfc7@47PfV^MM~_n|srU$11Sco6LBc<nia{YQ$pD6AyXa3;
zgo(BXtp;`6*r@WSE=l=IO3qQCz_TjB)*IP2@#-WF+`0R~w2-H1;|@fE)6a{CSKJX2
zs-CXGuLtVswmq_W`}py8%(|lpl3{dL->KbzFh05&m!EUtAYwM>wq?G)(1>q?%$SfY
zcL~Updx-FQ%O;K+R6+KXnr%3x!n1<3n*N<!=gD9FGHbUkJM4c#QxT)yeh?oL?Z)zb
zXSCqAtgTdwLy)MwI!NjA&@d_;4V2@hJgURR#lGuG%($@$DzCNb>#2uHLtq^-jK!(1
zjjj&5^L#+q33X*ajAWk06v%9=Ry;Jhi;d~H6x~{PMSN#xHs*0N?<|vvW6*!UpP%&b
z#}mB5Qq^FqxtgMX1?i=j*VSa>aGyK@NC3QWMJWkfCRi<~lraz+J05{pxTEcp9~5~T
zBmou3m%Zi8pZg3<(CNE+K~Sw^aNCStQ_%@&1RCd9iQvriYTxNNb%rm=$D;HO@Rh|w
z_fjRxkHZGog`4Bd`{u7)0#8+3__f>U_XRqdlnt76#5D$@uY^0h7Z&=|gghE+PSk|X
ztgM?vp&UzLmiPv-WsX`fg=)%m5-jK9LK-m<n*0o5MWCc>A_2(OpJJ7>Ezcgi>`D2J
z6$6dYk*yxfh#2PTe<qkdkTgnuVtkuhAhUvdd{G|^PfNiCPqcu0Jwgj_1*+%mNtM$3
zT`j1sVoDd)VhlSB<X%FFI0M8)^DK~Y@X<CN7%vz2acFbWFKF62&MfoZA!If3l~KK<
z)N^y!uT(U>fXdZ63Mrcq?Lx)sVuKRXhNKQQ8suwliE(I!pK@MoHZiKC^hrJb>5@_z
z0-vEFx7APO$qe6twCLJH^<^n^06m3dMdK+{#r>fXkt7k{pWDvlP@s_QmepVTX#Xi_
zK;sZ#&R7)ya8BMek3bgyOe+Uu6V`)<097ek9Dmckk=jU3Aw0U=4ALho!f-x6jPz7!
zl>`dMX#4F0^GOl%sE~($6GCT}^vqNEi9G(Rbzs}IWH(bPb8h!F;Mn;w_&$&)^5ASx
z$q&X96{k^NQ!U4@$L38kWf>%xg=tha1)g#OWqPl2>{ro_6Q#A6>VQS#gLxy_=AUzi
zKNF<2zc~(0p25z0n;^zEwJNPI+wQFs@yT7La(jfR)c{CrjqS&pQY~Jgu_G|i(YGo@
zC+y(Fh4khoZP4=<cdvl>Fzr~r(SKaC*2K;bbzOD0S8z|Zha-%Mt(XFqBa+Q)oa*;4
zCVy#3>nJ5XJC(f^>Wf;IEeGj4o@yf2wF5ajTc5;jxUdRQe69l*Ooc~|rj`oQkCT_0
zEKSgf=Y~FWG+f%zhcA@bDpVB3YcdMiU{u(*Nr3{Vr|Q9qnZ{!Ra(WiLxx)~n21sY3
zSg=*wDQj9Vo?fr`LJi)M@@50;vatIE`Q%DDozSh5%n~mbBIaX!T6CjpesA<7|F#tx
zE{Na3(Vg)VN-$~>JqPl~cf8e)zE8G8p$9JeNh=5+P9J6X63NT8NVd<|=qU~OS^uu~
z1608+cy>n-SB$w!tbRgT$ipjW5z+kcz7M(x<U_LJ7s10?1gnBd*3!x2wQ7<XsaeLD
zxW@WfS0%uioENa7{uScMxLXY!T*&LhyWgqnAn$jFiH*MjO2eWQ|3@+hFTZG0i#pT+
z(1&GrW{Gu1!>QiLBe>_yOldCO&g+L)#B8)+Fu*Ae_Q;KPrLj^A17^&uI{sm~kx58b
z*fJIZ(g;lXTO@)0*sm@j1+_X`I|99gVfOF}RH6y;YRGcWMc{f&c*wc}@IwYi>BorG
zA1W{W_18Wl@#x@&XG_aOSqE}`KSkDjvh7xRXz@oXe20K09*SUV8#dec<lD>cIW-SO
z|GQ#Ng>JQO!-c+@;O_aCw1hyHuLO7onG9yK=vQA#_|5co3@uK{BR6z29YL$*nQi;4
z5brf5LZv;@>n97)9=_G!t!SbK4Y|E8*110g&nq83>_MT<i}eM_(tZ})l&;Xzt~)@k
z5fMCg)XfZ=Gr}VFtiUV?2y^cyPZXAtQzrq;`ejbz3-LkIOrQHt7A8(Ph^|I}?6jhY
z4Wc-``eg5UygRM8JC|vPX=vcnhGUQigZ8movPbz|ck-|=IC$jMZeJJ-^^_ZRj$z7t
zF+0{Jb8kT1!H(4C()O9d>I*GyuADHONBSb9vujKZ;$rHa<pzOxR*yI!kOM2>4cOkx
z_%Ye&8qBM|F2Zz##0oc{`@8580<2Yrku|0%6kO&K#xX(<sJJiS*6Cv6!T{W=i)e2f
z&zr{uQ@S_`nC>aDr7ly_mAn)?kLz=?I<I`gy-uwuI=)cAd|&Vz+bN8|C<;Zb7NECP
zUzxTVfSkI^Zd~v;G{8+W0myL}*|9!y9BJX{?h7YX_Polu^2RE*2YeJ05Ww!v%aEIf
zaxkm~FTiC_@Zo8*7N>^S=(cjO3L^!lmlzdjo4^)?I@={(i63Pq?S-}FdfSTS>3RV+
zz@O3rOGP3(102%XBAEx_qBc%GrR(8(b4aTSjD}H{7g0#UDuloUVj2GuVZR+|9c{=@
z8QJgqjQAq^ht9M6kZGL})~DfjzDGN1j=IM73HaxwQ!p6O0DC%8_CGKz)IPM>E&&H?
z5*6;x!GO9}U$loadth!K1j*8Nd{5pjteF^tDY8YxjZa?)i^c8qQ^$t2PqRKi%Zy(e
znw>+z*J!8SKipjTU0uz)!arHHn7@(ENHFa=`RuyJ*qB4Kwsn;yHGw0`0#RXkPg&E7
zd&oBAfl6%P2$!3Ekah*7-Mhh;bs%LkS8}Kyu+$B$n^Dt5R(ZBb%v<Nipq>tgL8}x?
zncDBW*Ilw3{*+FC3fc*xP99x+xKQVAqJF9YUdn%CF!Ti@!YUlO1h@HW*?$;OM`J&f
zsosVd;lNT3ZsVx6cl@-Pe!#3>1@yk3@+DYBc}(=lH#C=yQ22YUY!a*X8QNJ#=bAjF
zK*xn%W`Fnm9yK-z8uu&I+>#_^nKBz<&+V8~y_HFH1tazmuCYG3JXiWfC_B|Y?$NR;
zjF~r^^%Z>m0VY++L@m*a2se2vsp#5Xc+s%Z(y;;7k~@0k|LzNDI?X(G?ym!>e$A8K
z@rvIstVT!X?NZ<ufvHxk!a0BgjASjhRtX0fC0t4;Me8}M9Cs0M(fT4H{tntYpCsmk
zErU<zO#BwhafJ1gAVr~43fPQg_~}1^A2Og+I}NN=$jQ%^?zu*Lr~Q%1G8~y}jx-DH
zp1IJKUu&rqf}j_s+iU{!GX*ef{%&8eEo_5)zd`h}Ws<?kFvuhv>5L+;{D(ik2uI&U
z3W?LEF6VL&({8|E*?afNpTHrS$2l1O8{U`GuR~z&ZizZoB<kL$Xmpk(3%$0dJze&_
zXv@Zis+@W5NPTx;ij+INAKB{FI4Enaw>3w1$AES14DKFuyVgpEI$<q~{o8Nw0yLbs
z=a;)dZDz*LAU9#K5_k$nv6$tJ`$z8`JECnO9>b&YuaOJxMGHUvTuC=A=l0ov3c=12
zo<vJ_BkDp9;1v4OO{$l*Pv~wz2yDDOd0OxMqcHfdT++s>2aqqoXQ7RLjErAhVWmJp
z2S5<d$Pvx6?nQW6faM^CxVt|elaVyIh)OO$6n&WPDbT(&!Pzi{h{V>3tv-FLJ3N?`
zWcd1-ceW@H9cVr3v5=`tdkM_UK<^qr8D%6vmygobjCcvDcyUeE?FmHWt#!DNP|OJt
z@k9o1vc3Bj()psd7K{={1YXX8DuaSHN`Aygh4w3`o*fQMF_~BQ9d(y1oY$4f9o4Yr
zVPl`_&n4?*!?o*jB=c=~;7*2_8?rb`&#FY!G4s3g0omxhcoT=yAl{s_e-OIO{B_iM
ztaro6nBi;O{EYsvH@()ZgC&~RsN?Y`AnV#LcegK>GBw7hDcclmM&VT!2Hsg3kSI-!
zER^A8zn+WO9+w|TxJP!U&hm^*1E-zOp`ASz_`PUskncf%tNM3cwyoX+@n(j^3ARji
z=_P?a>%lxO;mgsFI1D2`;H0IkMXo>Q4n8QL3<V^dGRE>7@RvqiCOKOQ<&)NjQ1JA3
zSTe(cUYw85<6>#OKq{Mr0Kkb1MAGgmRHWjGod=`0q*l2ycaO0Nmfs5HUEmRvdJ?Oc
zbp|*Qm%i#NYv<NhI&KeQGDI^+SdY7D#kBl(d=+<CcB6nCZScZcYBKr7;hC|Gc-TXw
zzMP_!(YB1K`>9%a#?jJgwBSC%^V~$38)5qp1tfM1;HQ%XHntL@iZkCJ-u5<SynG5J
zq9j>`_Bv))wf4N1Q&?wJipo2}hMmFFa}dB>_&sFN<rTBDrQ+B<gPL6}hVU_CHyV&Q
z1+t%-FLloR9iWGWqbw>|d(Dv0VMnoSeo9ul(Kv$~EQqaX^i5-hYJIctE?5R^YhVBQ
z4k$RQe@h0kKc|l*9IMPD`Riv~epo%qe2e_Z01&F9p69?yWJdhqH&DUS-iC>0ra3F1
zA9W(hO*YEC-dIy4&5Mu<Z<R^wDE+?s3NwIj9dHe=(6;0u5d0?EpQz(BzAzx60CZ*9
zGWeN2h!Ixa%s1Y+$xY#jpUPJm@yPj>);CeSExG#Xmb=Z=HiWTcsV+yc86~J@eG<|w
z--5?X3N2?4ytH6Br*Nr^bY5S`NBMI;GP(s*IQib3)$c!-LoDy@TkHF!n4U$RW8^!6
zJNuY1y*)Udl%p#ZFsL5j;*VuF;~n+g?)RPL9iqRy&h7x<UDICRZ;*tSuN)i5l+^IZ
zUSS%PX{$<^EM7c2t%ampl&!itrK~&v#|5jkIsK!Vt-z%gsf;Wn*Hg-Dx>a3b<I~!z
z+Pujte&>NaBMzYux}tQwF_1+Z{m72+b+oa#n|XCwzJTTsukjOme;|iLCz9CNBn7aX
zOqWoTcT7B>Y_^}CR0N@?cqj2zUBwY(!Q8DA2;Kxn__W2B)~c=2fQzvD-H#zD(dN^j
zL!1bZ_LW{vvvAu{9)(wY)$vNMr2I<WZaPD}E(cFGcr-?g@l6fDZ09|gnRV}xjSDV5
zGGSZaomR8DOJ=x<kwl;ENs-9etChj|aEixN>D0wa5`K_H?r_?sir#5KMOsyUW2A94
zK!8IvzvvUZ-x(G|h7{M{_aKJCHWB}|hSn>^Bh*ErRCa9u@(XHKRh|qMo^?ht$bf7u
zoJxC)qnLrOj^;QNJxeR`tq^~!^qr8xve^@&2{@%d6(-YHDw8QOWUYn;{stwwQpl=H
z>$RRXt*eL&&~7wT__u{t%fPxq<cauWJn!<^AEKwSGDTcAum1W7Ct0-<l<i*BZ`LCe
zuXoyXvKRgNt^~&?)YCJdWRdwtjc`&Z$Svo<5!4XyDR4?n(?W)@E*msC9Efw2l@4kZ
zT6%!oB*g0`Z4<L0(k?*gn-3Au7c}&XO^@uP@<<>kAjO$|-I}m*lTY*(KqINC`bbP-
z(MF|CxVB$!t~kIuQq+R(9>6xQ&I0tRNHd})>8NZr;1fU=)!*#955<C5?9yMz>5BUE
ziu`N}TAX9dRfgywZL)7C3EoeJXu5PmF54XRp+R2*^`cUt?#Q7qyKi7VmvEOmHV*QM
za*DOT1#ZO=EtE|hnl>1BVL{sQPIwpmFni^DP7LecZ@Oj5Xnv88X8*nu{mduiqBY?B
zrCl*SnY|JB_vd@enY<n~<tOVXQhq3wZ%v}2Zf4_cDDfeE!zX|d>>tKyNV`?rl8V2a
z8+RzY0Y;kNEOwtrHy%|OxTM;XD`Ll@KIXbILppNXyBMYLE})K?E+zPF-npTttp-+z
zRDqVZ!;36&wm(eWw{)>~p>w}TNX-83<<M|*6-?-prQh_N$RP_0u-11kqUX9_d0Z(@
zEw~L6s^K9)My3D8l#MY<Lg*<ywkYI*LD@b4(1{VN_H3)`MBNboiVZi=5ncg`KK#61
z%Katht_`~B9OiKW(V{3<%ydB49Y^jAogO*Ap&;-hu?6SxHbr25mZ+7LF-L86ZLxjs
zVYH+Cq2N)(Hv^lSupio{h`K&l7ePI`VSK&n14$yCw!Y6UC_uXO^Zuz{hWiK$J6LOH
zk58dcKYn+P{Q=d{w?Ly#01%Ym%lPPh@rC5+oj5HfERJ(uenzzVOfac1B#9q!?fXrE
z=#3G!anLN*zh8felNpz8Qx^!t8{cS8yL;V0N+uKt+cvXjg0HHR6{4@-NyP9LkybX|
zm4^2lsUudb6%7@p@PtcOSgE2E^AbaH2<%eil{$8wmUA8F3w<!n>?ET@@^n{?C4-Y<
z|C?DICN#Hd2%XXZCEslY21TjnZncmPE+wCEPQ2lcx+<oOG+zh2I$$eX{K1P%gAUu)
z4u3=Pu8J<|^IrVSST`xuC4F2{27}9L*#OMD@w2tEkvT8|bHeQhvOTiL8nHjs9A1*C
zXzME%c;tjwxro$r+4XBjw$6r-pY3?;Ewve0ePL6oPzZ^JOl&I0$3eZni$deP@@}Sz
znXwehL(ffWAnrB*zKs=RwkaneWl}oFB7E^N>fr|qf-x-v6&HRY5Id>TmM(Iw8{s7Y
zuwwCplet~%WdDHYW=i7*>?O70t}lpC-<qC#B3qT}skiy|4rg6?7D4``@UC-MtPm{V
zWM;?Q+94QY=FvSfeyp~YZX^`Qml)hB4t`>I8;noEP<u*06Fc_A9ez-=4y!N|Pqc}u
z=o2$3&@1`jAq+jodW+vKh7-N=;;>G^<uSX+7Kg(i8Oo5q7|p;LUQT6ehxCJtpklM;
zo7_R^zW2{CAp}POOHft&ZiK0ZXzP{E8fdTlbIuJK6_5Ytj*P~)B6TEr`PAB?ZZK6+
z4cX446a!Dhk09zv%nxgq@)_)8{@$Y3&y7N|oJ$^!z=eKOa9T;~r0i*V^^i#2p|v~*
zoPyBgA4@h&;F%&p?D?uhzw)B-tj)Y^iq=b9^q_=<D9g2{&zjrcSBMf(IKaZTmlH-c
zgO@ToKgg4KbW3B8c`40=^MW4^oPdllk$kl6^sCd+D|)z(!!9me`@EIcpe3N#_3D)J
zg^>xJ#H|rsZ53F_#V-xl1Xw&F&ZKN>L~$csMvmSW4Qq`=K-S~ZNRbfEIiG+jk6@1*
zrG#f?BAaU?J`2`rLGEu1pB}L8%fLg<M-G7lt7E<=;5d1eyy)UTb*naLqSm3SPK^*9
zQ=f2;yYvr1dorU>3<M|AARs6)$wBZ!#`q>o%u`tRm9|3_^t|AMcd<rDkuvtv8d4zK
zbmh<k_=vk@gUcxzaeU^@(fSw@Q<zG;`p<<3S@TD&L`&FPz4sJqLf|_um!`cHcY;{p
zypT24XFNQR3tMTbIwB}5M8Un(ZPPkZowXjd#+^=LItm5D`id)a>PEX@B);(7!<6FQ
zprL_ym%GKnK37HFxsG}3$8k?^ZdaLN*781Klv+o5=?@@(Lh-yaU+03;a;?J3WGG~p
zDK;fA`GK=gZ%bw<5~>_3K;J{<52}?+X|3&J5lyxuUQS6bI8ai%!h-|#^@J6kh>NJ!
zsSD|n4{N<If5)`AuYqR)LXk37kPJpKBtb8tBHaE;C@GhAlMf?JT6ClA?qLIdT51w6
z6qD(?SRm^KqW4>L74}nzb)=27aJ$C{XsR<^SPr^n-gK>qF=4XvQ!XQ(-|@^qrnE1t
zz#Z`gYriaV776hxko0T{#~ncy;rd#&`-T<0{O#)(TSnnpx{;^ky4&Z8>jLEMbqH>c
z{>8wGm{!Nk=q~S*{j0v}(p;Sb$M@Qt_Yg86^M1uV_<GoRV*IMpON7w(q7VXb=yjWg
z#HH%K1pm(rG6$Ss9uK3qN97YCgN96%cI78UIh#6&r+on_{jby~*Nw-7e!&syV>M8=
zcMap~QE~I1Y;s^;y&E>|ucOPgJxDk8&8b3cdp;8}WF=s2X}%<NCWC65pX3@|@PZ%T
z!g}!*FPObEjZGPrIuTVr%Efns+*=^*V)y|*e`)6>n;7z;H0~<-z9LrEF&G_|=!zs-
zv`S;H-<ver_xFzp@0YdFESXieT=mlRgLIOgge*YWw~0>S02?@Ik_n(}S>uuY)*7i-
zj`eF^Gywerb3-=Qqu~HIRW>~?`_SMyLQQDo9vQ!3UZ~3}8&D6uyGiu#Infy_ZuWHm
zYBu()Q$o{h8H(twt*pWEQQFmIkM`4A#w;yvwpli`h>js%Tm@`ZwB(}e>lHns0Ry#H
zjwc=G5w`4oblFg5UkP!_KC6%lYA%=y#hCgG1(|%nQak{^gXJ`y1pVTSZ7SB47%ZM$
z-wKwvhm%}88e8WJCnnQvjuJ|=A}1ko3-Pqhs_EEs9BrM9Q>(zx4|+r4A-QUR(>;E0
z^n|2=2#x_Gk)(3jo)^bNd6hk)=33I)wEPKm`1yUfa=DQKa7C85^^G~UWsih_12ju?
zb`UP7UWz>a=#AFiaGt#DTKfR@Hhhcgc?vz3pT`0|TVA~%U$9YBc|t+41{hRBP5Lhf
zYh<)P63UAXRQ{wd>{)MXClK)%V@)wolubx!$w8RLJPZ^Q4q6XtY!+xX6jjYUvW0+*
zjn5NBY17qBpU@5NsY<tDa2AKKnU9PjF#~+C*soJ^rK<vzx+dah@6<|A5)?NBDQNbE
zl?t%!$CI5eE!l%JN^(YzeIzJu?(Z}>qG=ijjcwgpxm-MeL-&8@Dzq-i9+gwP9yBWD
zAdn;EP2`O386SEapZtD7%@y}UccVw9r!Wjmz#@$wlU+QvupbwAK?u4#y$k;?LjQcW
zsyV{7D`(ehE|N2aUWj}m_m%-<e7lYvZk#@l*b+=3K?6QPHMl!0e;FK6%_*O=%sNon
zzzA>V2s?o*puksstF!q4LGISkK;|+a0wdR|)a0TB&|MXD*-~$h7>gW-Zn4c&;9I=+
zo(Ly*BpvCQ%l$eFR8&g_X#*u;|COKo6nJZvvWIM7bE-1V&HKg<nmep^qR47lz9wvL
zINhTJS#iD>;HRlP%~W#h*c+<g1V9P^_e@Wg_X>jNFfR2OmUb2Fd=t|ByXgnL%Rc2R
zR&2>$s<nul8Q;suTbcPyGMbX9*$CoLZy;?@3rH98@uX#ir8Ek*Dc|Q*Qr+p;PQ$?r
zgYkJ2o7?w9x?n1udws#KnMt`CsiVxAn`Zk9>N+FHsEF%zu+_~3G!4yBxHelK$W&e)
zV~lm`$-y}cCb}0Ve6wP{JyR6A(1gFG2yBLy2WY=lMl2-$nMs%al+l3en3y<vWw!g)
zHMW@skcVi=?H%!B#MqMK+Q;bPHtZ-{rL8BSoJp<)piwA`QS+r=A!&C15@jE?5d>J7
zjXc2>;(^`~=CW*lV8zAh-3(-iY9YN+yfKYSvKHgX)Hc1R3d6V{>gZPa*5_e^4^*hj
z6UyO(wMOfs?M?2~DRDDLfgU1teitT|M^X#WRp^sdSL(EJzC3@A%PDy?pH5gzC#629
z<WBWrG#*y!(y5m|d|+e&!Bx}IEc9WdS*QSa3wzP?(a?7LLMo5}q_6jF>S=8`I26X?
zf#sxDxBA9Nh9ovk{|9`B&pypR?bPgcFqbL0er7SHot;!9`Yw`;y%L2RyWkodk|q%$
z08EYR(;!6<N0ZLGkhbqvzJPhAvsXr9YZDiIAbB&l1jO;XL}bYv3hK)u)4AX+%E&+;
zAr5~C*|vR89md}){qMJM75ap{>=EWxgIUj}O#IE?_3~3wL5B2}s7Yp{E7zp;5vd+z
z4k4U;oe%Omy>F*~Op*VnhuI{yHiPx_w!<;cY#Y3z^8o(w6i*t~4sO9Fjgqb!<PI49
z-kvTn0HW@46t1FDRZ820ID&5nYD??1%~et5W>zEm@QLrFh>YTi#Pw`~jyT_mZVgP7
z!2hXjrH9}lrucpHjilL^7H5c5d{#U0VGgg(MjG3NU#+ST>6Qn5W%%eE?YPd6-#FPc
z`aYyZ%WN1A;GjsAO#7Cf^!Waz!B!#e;Rpv~J&t*cz>m}E2ai=8Xz182Wooj@Z)3st
zLg196jeIf$;$8udDzFtRYlUZIfC;yqHmaQoE&(s$%|?p_oce9vYzgy(rXzD>vm#ni
zdrxoVX_mV_6w@uQP3IewRrxuH8CR&60{U`MRNH*qsq&G}xD<zHWv?NY&=vtY#=Y2w
zI>?GB77)!{Gr*!95jXjXOqEWC>#r5a0#r@giY^-f<sB7ts+B{h^N0u;7OChtcGi7o
zpTniR0tPm!pxW=S?7}Q{Le+sVpkg1{w#nJ<;6fmU`L0vCFAgcAi=5Ebqi+Mm0-dx+
zhLj%Z5z;plk&SnjW!L=eUByJRuPoTSCj?<sA)6XS61)``QVH7m0%C-B%cYQ+tsB>Z
zGFwqr9Dvg0vKH@?&UY<aw$ZHMxwv^ilhu5xI_%WVg(rqB(Xr6ANso^(gRFUV|I)Ie
zfIxkkuul8=vqJv$6bDW<G@O5i^997Q*!{-QzCOs?L@8)!0ASGaSOmT#Mzx&;-WEbQ
zXl!i49xSKy_Y$?#GFxuh?f08l)gi#dhB0H0%O_kLWY&*lB`#sP^YS||RE65`YVO&>
ztia$(;8oQMD*^a|dmE=col&X}<IjtF#!wgza)>MHHBf1Buy}VikB{O`L@bge<tQmD
z0pgx?|Ay!pwh;3Wo*M*^cYmY`lYkCM>YG+Z54On%Zgl;iD9^j$J{)8Qp(A=)=_m{p
zG$?ib@seLvRPGjjven+z&4~c$)w##=ZjAQ48i5XH&7&m=q7V+RTg3w>M_viMe8Xly
zyV&xxV__Ars0XAf%dc6gn_Qv`)z;svI8ks(?wpd~NKcASjTt&zH+P%Tc1B1fDI1jw
zQ0GxlIeUn{nUbT2yz=hRFlw^`qNYg+`pla6_U$O^?~O~Q_$aqno+ursn(=NO5QQU;
zEpH-OfZ8sz7o90|BDqVwim|FU?sTFh40e&FR&dMpskOqYDl5fg6h_3kVx0L_87*2L
zyTi1Ne(9E&5Bki%<_=xR12P>Ks((rP;yAczoBTb(Mj#sO2Th@~6@}PRczD;~CDgTs
zQgKm=b?kp?v49yaDggz)DxeiA-RBZ!RPZRx@Mipe04(_Gkb|=t4~!EagFrccpxCS`
zq+1<#rnYh&6|G6rMd?@VQLyPz4_?$HRXOy697C^Pu!>|WSQz$ob2IDwiOJS!^s(w4
zAvLH|mFElDWb!*o>S+aQ5jzH-_uW`Sy4jtL20=q5r?pi^<zG$?Q2DU_Nry7alNfVK
zQS-2bg<M#$h8o*xMksj-qhR!2aJ^nm3(`eKjW(C=<daYZ(tA#!)soY>t^XD#73D*G
zye%3I`gU>3ReF`X!BR6Y1tkVT7s38MU_W2IMm%g3nr-N1<97GFQ*#Gs8=)r2NJDrp
z57}9jK~-Y7@V^ar5S~?tz?ze4{vLCgO6GLP-TE#@-8$OKJ-3>lvuRm}8KIl_m^pYb
zneux1zJtX($^(a(g``JeoNG$M!@Zl~M|F}rVc(b~&la_$e8Jco-=lBqkSswwz_aG=
zSb<LcLz5%RLp0MQbrQ=K=&z$GABJs5$+xZAtF!a;V)32g&9Oy=I^FAF`Sp5ob`r)&
zXA(9*)>D>-#$Q~aGBn6NBM-Z(=PopuZ;fczyjw~kxgruOW@xiZ`2j7|=@|;Ogzit2
zOQK91-<ZwH-tuXS%a1~CF{)$!{x}d0-eYx9U*6lX_MB^Jj~!ofcgoQRRU}tF)AR$3
zPF)?ed}<xQxc|CI-J^hb5X^*COw>`Les}Kc(N@;k6)X9EsDM$POX+fWML&RhyX1b#
z8V;QHSWqSv!Y+$vfWZm$DAM^yz@O4({IzF5B&>B3c%f=FN;SIaB_vAma~5I4ts@bG
zxv+nmCAS})m5iwZdC#?zRjHgmK}<@O{Rirv)GsJDOf1<7lf75BnqtN9NU{!fAn?^N
zfx<$wD((3xU<JW*Q-*haKQ~>Bbt`>ny1c$|ap&6Z;N$?E7=P#Z)C6$1WBdjAo2UGF
zD>ljxW>;zEWW(mYZx!J**-%~N$aR$){ynK8Jp$`|SFtAmg>`4((+k=)-(jaHPr@}K
zP5z6rgw2)8sSQkMv~zl=NY?2r0m_=oSV|@6zBVeg$scCk-B-&$qw5bJ#E;M#J$?}A
z#j?q5i_>$@X`8lR%aK}@eF~PpaZit~d(GZ1_A7*xbXL|#Rs$kBDOdV7YMbpT?_Zr5
z_&+)^87N-{X2f0ye7?u&vlSEVUW`;!W4gUmZ-1}Hy0>p#F5kFijac!(9SfCetOLnT
z%V*vuj}BxHIN!g@G_e2lby89Qlz%-40KgD40Fb}vuNn;i^Y8OlF_-(V4(~s`S}xH4
QqCgM1!2iSlO6&ms59p+&;Q#;t

literal 0
HcmV?d00001

diff --git a/public/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/appendix-dsf-20.avif b/public/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/appendix-dsf-20.avif
new file mode 100644
index 0000000000000000000000000000000000000000..aa13514f37adaf93400dc0f6778aae1684cf282f
GIT binary patch
literal 16574
zcmZAeW2`9A(glhx+qP}nwr$(CZQJ%<+{?CY+qUmM=e+OT<j$nKMvcPkSxKk=^Z)<=
zz%g_2bTD+YGz0jzY%R?g|ATEU4gdLorM9LnhX3LJEkX+u8>jz4000L|W0(KK|7XQ4
zZS0Nz<A?|V00RL22LX=%&jJAOnF0X)hY_IrKL$YPU)FyZhyOn#_s^BJw6y#0qx?6D
z`p+@_Z{fdwhHeau|Ec(Y$$ydmFA4acI#G}R9S6?A(#7^aCI8*xkEMf?!#^Bl>0oUC
z&%zkGF$xEQ!2BnUV(IK)`X2xQ0QC4T{o)@$aIo~S{0{>R0Ri#P{QvTC{zLui{J$9B
ze<B=)t{%ewp<SKqxNIFv&HsB7;xe{0cHnaMbapYdb>{k)V`*b)=;X;|Xyah<-?8Fa
zI@tc-e1QM}fdhen0f0b4fI!3k^`fwJGX38q{%!ZK*yi5^0sngtIAarM<Nwb1-&FX%
zZA}bakN}{dFv;lYrA0tkun?G*d}Ja#JdChrr(ghe*(Ouvj2F=b?tprb)UMKyUD_R2
z{c<g1+Bl1u)M4()3(Ig;;(9FovaZ5ptNQ%8fPRg6<6i7uO}ZsIUA(tHp9xNM*RfCS
z$*N0*#Cf)1B$eFX@i+Oo!y&iw(sj><RpIw_QuDlAz`mvBT+glu>pi3ScEcJ0e3LSk
zwoC@T`wV;=Ki$Nitu7WgYOn*<>{Qjr0TV{Y@Jzp_c<2gM6W{g`B1J?<!e{D?+IByN
z@URJIo3twSUvnCsTsynozX>|rXLjuNR6<OS-8)aqV%&Rq<s(2ipE{gX^+;weES%KW
zOeQ<+43=y&!<YhBjd#{iLO__sf745yyk1cd&`Cn_S4Lq2l>21kG^3>18RlKuPr{%}
z^Kv`^`gWY=j>^m+$HYMv`ceCq)8L6F_02dgAfA;@<BrBwzN$)q%^_d}=m<Rn`<&+K
zI0tygA&j-Kl{=yMN!6o#c6u^eYsz3xm<wf?X7J<w-1>A?k0=Q639avMScYTu3dQtR
zWaA&jYl<KrlnUp$Lc02rCk+;FM~-D;VYcWc(sTQzqDnjJtJ$$d8&2U-A{jFVG(DFb
zLGxpaQlXXcR=vLb8CTlHhN0e&oaLQln!<jDF*-oG^4A%JXO*s=&rGHODkRoTxTTeE
zQ3(8+6oSJkycZc<eWZHmV8(dwx_i41oPtDSrMU{PKvjHXzbCQVWt!9-8{Fj2(Pe|-
zLv~Ea!!qujfYLB9-SMC?$c(!iQiS#ylUsH;`w-ln;c`(4v%~0Glwq`{<G*R2wa*=6
z?r?e+JhD5#%+-ndAnHc&5Ym*Q?<OR@3e-3Wn2~=yUOw7A&FOV@-X)V?aLimE#-|^6
z($Q<;;4o0%$<Y?VNHHRS-}&UYdzfIxdyI18suTq>8?H4iVR<Z~iI_$|Ahct{tRE23
z$Fikvv@^uqFQn>2{+i6D_^Ugb(|YB+{Wx0sbr>9EKqAOkm3!vBL3P@Q9ex{-GJ(U#
z^<3BKwZAYQnBiq?fzATxHcv;Fc~J&$8?M=&<{i$MH~`Zwk}DNIu12c<YDbN&hgENG
z1rwSmN04MT;Uqyjh%0w_Fjq|7H2YJ|X`j=wfDzvsn%=hWp2ga<1XO5;_6`0L$^$C5
zHk4RnY`KE)(>_F~JW|4l&oz`nU%OBr2A>`Qs-^(*GsD>^6t+E1;;c{7UC7EBqD#)8
zFKd>H=cNX}W~a~Q^N3ppbJ^R9r(ZBS_lR&vc&fAZ;zrG}PxN&0{4MWjZvWJ8<qQcj
za`PU)Itue=;k<&>zLILSO>=efERdyejHA!0>T0tH<!DXnhhStqo|`@o22iX@U6GGi
z{G}Le2qEu_UHTDK6WZ$)Q#>BX;6qV<|I=WCbx?TB$6l4FrV**kfhq<;W<MgBb5}pO
z(X3G}|2Blp9nLz{ZaMU@k~V7{yIH6FI|3wSsjKC919otKsA@Ho2S{MAQ*gasYh<bP
z%j&Tk0}8q&at$r<wX0eoY8(zO))r${-r{iw?+7SeKj@4yib_lw5(alfel5TKEn{le
za!+mzQHy;!Xg1!^-fD7*-6AB-Tw8<{p|e*e*74j--`sY<A=gDg#}96d>a+?v3i5=7
zJpBu66~5$7uh;~Rnw}<ucP)(qM{vgGN%i!q@{u2Nw4~`?fOIu5FYSa1w1+<>mB@=t
zXs|c-y<_FE|7em37z1L~+?HvR_Bs1809so3gr?Y|UYOzY^OTW!8_#>@hbAgUBkv(X
zX?oMt+!Bs<`e%taEBwH3pRN(0`K%Nft>krYvvp?zZF15m7u%ZkvR<+o;c}_ykc@|j
z!v9w?i|9JE<VH4+>@9N3*ge4|3#LdE3fNw{pl}^$A=j3#6!PVz*=o6b@11-9P#8^7
zXEqs%N#mY}--~xbwST^5pk2((8KOn|9{oK#18m`yQJP9Rw;g59lF|lRoE<6u3h&ng
z(x^KuP%?db*E=(O-^eVup?I+Akkl<ym|mPM#^En;(iWExN7)rzmLkD<?<>RRO@-gm
zke#cdoj9jk*=jiHFK6m2EmuIMao980@E)aC{ql`$OiCDx1hVR1)ztmX7~s}C56E{I
zaX3MZ2Dyk!=;+ockHA95Q1Tn9&KjLfc~Lr}q;6jo=ChIOS|=E7Bt6>*(2?TF*(O<5
z?}0mc$)oZ~6~>_AF{TOwChKmTr)7Y0&mjHuT}Dd4?JH;sA0}gH&*+roA^Too1-Hmi
z>C#(o=Gi4yQKW2X9lf<FW(;a$*0aWxS%C$Gj*V8{foF7__}w`N_V?5}EjY(aYqNv=
zffHoT^&_?>n;=!Fqe5(->pao!FTF1P4S%N-J>ZHCqmh24YZ*c|geYz;!ymUKWlc%p
z{OJa8Fj=O3KzAq?nDi0XzJ-2Y+w-(sQ#LyDqfK`yDqcyhBDyz%g>8aO*!8t=GT7+$
z;xbS77b(I!V<At~quc}?6SQ-_c%#}y(hlZUtllD8Vf^4lh5d3Bp8=t&R-QbKmAJ5v
za<~}%VcJx}d7h>i?3=EN{JIx)iRt6ezH0BWJQU=Y?%OtR3T=%3WJqgz?ZnONtPvGo
zKun@tQaia1P7UR2@(9!!S7Nkhie2wEy}f;Ww6Rt;2tXoSBsqMjfHRb7@w;Y}MA2Sj
z2;zmWQ8E)T1{-#D!4r*@vK--n(6NX+jJ0{O))v1r-!GJXSJ7qUoLTU^oO1!s(n2d%
zpW;VBkXP;TgLo(K?bMEP@Iyvywbi!I6q~Q>26U|7!WbtACe#uh?eF4d&dVM@$2Xfk
z2>BY44fcb!WbY=aLT|l}mmw1BbKE7KvLFp?<PP2<Gkt9Au$6i$KKgG|f!u~Dzd`gV
zEJO0Zd-7yFg`6~;5`W*hv+#y|PHtI`4pmK*F|aNTseBx6k;@MTcBnWakb9D%0e_6n
z{R2g;#=JNl;;~K@`ek?(_A5oM3!vAM*hDTuflHy0?%C3I;#yW_QYcEH=1wtYwGC86
zeYmrUyyas4GjiV*kUL&hTXY5hn4&n-VBr*_GCLkJ=7xzUavc9Ql|oq22nkWLjLY4n
zHHf8K5FFcw{s2Sq@%7W|7m@444Fvh<nN(c4cox=ebVOoe{O-2(;t4i1m@rw;0zdA9
zYbIt5iou&_j+~hE5^XyFE)Chw)h@@wDyMqwAF^`HjW}IVK_VQp0ILYSIbq;QIM>b@
zvADvE6=*y1)ZyaYSZ^C&{$+N)M33vsS>93HCHbiVH%)veKhVW0M&MdJj5pad${-t9
za9)=(1NWhpPQ|qTh8=M^p3S<yuLH6}8)fAUI*(p;$I{x$OAL^nP}BxH7MqN5pT3KP
zXDjrQ?1!WsQ%1)D!~GwslnR3wTLeBQrqfhxKOyaon%BuA8@^@k0-}2>2DmMym`kVt
z%Citfa5!YgNnP7c*hiJy8-gPYc~c<zVeUI70+qk-k=S4<iVr(N+^UqT$bTq*r3X0(
z4VLu)FcO7A=yWxpE^e4%enh`a-jLD2`h5#<)hL7YukVX%8IyzX<0pUc2-6gGd`iOb
zXPAaw?=_ZvT&Wm-eV?@f;BhB^wj6FMK6FXI>Grj(5`Vz47PV;=<6FJ3%!ZK=KuP$!
zL^({v4KqD(g6G_r8!jPbyny5`(ppDS(FZ$tt&iK_F5)v>-47;f?ku*{ruu$1>~JAM
zKYFD$6Lb>K<1s$1L%J9-|CsZ}6D3vl;e<?^36qtne`s~Ck@=v06vu-_-5(ev-f>0X
z2PDv`;GBJ*ZYA^J38AB?%6b$)MKuT@_{A{13jF&@&nK^nE55M1RjT4#cYUd<Tb|$(
zO6Aav3OBC&6<AHJ(1~irj*B0se1DPusM9Dzoq?PIg6=W&u>@}@@<w6!bq}Z#$JVC~
zQ|n#=L<AU$(N8<E=mtq(6b1R<&b0qsuqWgY-PtV*8Abw<xS=6`dw+suCX^A_Y||3f
z8D!|q%+yXwy{hD`>#ZpseyCF0;SDhMz%H9K7(=5<l<|eQb&R2qP$t%Oi=H~L0XuDr
zO%!^Ypr`Z@&}gS#BH2)1W1wYGc$W3K83qx6Zgeq|3XjTV<#w{I9Ip=a32<{Rob{1i
z&$6kx^kj;~dbYzWDyeR`>%`SiTNT98_|xeFaLeDIvU>>d4ZbClH*(Ba7!1#jA*S%w
zm}&#fhaw^$z&U9{jrzdxh+J0Uc}wD~c8J{=^K}1-%RrqJ4uxd<THP5dJKlfGkwW7J
zV;ICCGl%3R$*jwl335n)Ne<%xrnjAdcZPzGBD1C2_`9kIqIh-fJrMXjl-;^`09H|#
zQleg`8~>GPi5BNN#juDxYP?sW6|b)phaHna^w_%PYU|e85jl7?&4{;RFV{9V#72XW
zAa+E5tl<oA7r*fm$a09ICPqO*VrDBO9^*yedA`Eu0w?=ehHI_f=iy8^Ray;O@<f4s
zK@K8n>?J~9^Cjc~kp!ALoFm^BSpy~4+NZCpEXn<{yH>paXRDNjIKE5%;Wuhd^COkD
z5}9h>J@7gtWHCNpGhH3WkH<NuaNEYJ5wLayO4qtU>ve%5#yiH!G4-wY*-dHVC-DGX
zv*V!YuWL2ab!3zVirCS1DvrS}|6Nk=a7$q>4GwsjltDy=-xK42c=%)b4AclR=5dz+
zt`lyi^<G154gRk?<sZ;@50Jsp>`4cOEXL~MW@&gqFuZ=#s9fs8R5-<@M*1HzgZlu6
z%t(a>LfNLuA%^DWZuVODnf7yv!H`d{Zl7KD+ZQChUL|{**BPZgBUIeTxflT>N7^HV
z)ZNdwf%+|dN`Bq4Ho{si`wNOqv!h=tbPX$PwkOi3mE^wT;Xt~+`M0YQ;sCTL4P>s3
zO04su7gJzg6-+m6k%tiPh9Q;i^5jwfyy6*pT1KdL7At#6F{YZKg4emF<^g^k1g2ov
zk05*3^j^OPoRJWCchch4M}FHS{CFyl7Zgoqk7m^IWWQj4CmIZQR8yO~R+V>|!0yLO
zx}?*UQr5lMbCRt|jz(b+VKQ@-tU{-~{4YQw$XF33YY0EKb;^ck@#$5GPBGsZZ{_Tr
zF-0K~{k{hEnc7AL;=3hA(*8uWQTw2;LI%*3Jq?p2#npI%WprFMeOAg-BbSg^F+Ip@
zi*teWn<Y6;WNR}Ncm-XBAhG4K<G}rJf2Z&y5(S-|Ae)P_Z(Nu)@eud|B$ZOAGu}{H
zYv3~s^jWo-HS~xQZ?#}!mk(W4Ma55MdEIf&I^m^P3@Lkw{E_aVQ$lP4)iK|MAA}%z
z%>-Y8s3Uty2VzSalyI2q4@%ntqg~>jhj=MdH;7|NYY&(#+RQ%>`5s{yZGfXw*Fm8%
z_#GgRvo+(-nd2>8Wd4A`;<dCEM`=;}ws<<P+rFa8In~$XI`&ruU(1e{a1WDUrJtNC
z<s>g1-0t3TbD%D;&5$|dsJvp&(V}yx^E``TPEj&f#+RN1Ydb@$(K~I2p>I8eSUl<H
zi^IUrl?<&(_S~%|nu_*NYQQ$={@)#h^!8jlchSW1v=tevdf~}~W_)c#B-MO+29<<&
zhggu!_)QeOtdCh05>tIVFa6p+X(+97a#swrLYYXNj3+?{0KX0ORifc4)PUB;H*h`e
zXr*{j!uf`BJZ(^3d^+Qs(3kO}Cck}4A+9js?*rTgoK_W{kEA!Z5lx3@#7rfS*@zZr
zgj!L+&H;|$a8ssTkjye=Z4YTnk%uesZNggLWW&DzV8_NUnSOowRhI57x~{U7ZExf{
z;P#P##A;2Nrn&J#jj|$apM1Q64ALF@)z$+^X;jgXCts#oUfa_oc3?yn%$nbk#FhS|
zN`UU{Yct=Tauu&#tl57|4~%UiG)w$WvW#s<snEJh`%pM?O|QbcVWP1Cxp~*{b`4EF
z(MKEc9bqmW0t~$h-~yWdUXpZGn2M#!Xfy0Yu@227g~wZ;%*(Z!RWJON!03%S_zVG2
z@TZzi-@ICE7uqHC#R;^(sdex-LFWt+)ft}cst3Ljk`L~z?OMninJoA_t3sR+$L&%8
zbRR1fJB;33kqU*(_(pkKwR^9nR0`7c?PI}CB2|3s9h6SP%iTQKaGm~l0gJU6gJMs?
zVQ|q*uAY5cWB#J7JGPxew^~eJL@uZF)P&~9biFz74|v#UIix5~W6@LK6F;H04#Fv}
z6O(#|`doqhZphK?HRbU}VRq2SaZo1$JG)S3+h*6!J(|$l8-_ZzQIC7mk}7M^<vpJa
zz;oZvh@ay9Jo4au$lGsx5ayjhW2Fh!<kW87BWt^qoO=oC4?bs;LqxiJ)8H0uPCd0F
zFvdfFh`?)>j5M0Wl6g2Jxa3(vcr!m?7qX~iN}weQYlnCw0hDkp_nY#ZXtRk|aTIDg
zd0jNO*^_GnY21|8Ks3gx+ZDMdngne-*=10S7ZRET6n(qm%S2`_1-Gzc#|_SVO0f@9
zJeW8+!24xmo&d0T$CCxyhaj+ZR>VV6xDrCPmF_JjuB47H`Y$<`R@i}hB{SIZFNFO}
zmkTRD$7*dQ#d1X#2Bdu_1{xzc%qm6RG#`!)k}d0w?UWBGjygh1PU^<BJ{Ee0XXoE!
zz$(fH`FCj$%#Vc9&6?PqKGJy%_OzM$+SxuJE!n6o=dr=bLQZsruXW@}77Jbo&0Oh^
zIsrZse-2cZJT!B_Hu&i-y!{#0PlT$1Baeiv?gKu4G)%s&Xty#FMpEb2oj8>+BJ%Pr
zwc|_!TRDDSnLshKnGwudwoAAfRe67=73hpB_^s~UV@`kg<orxP7{-S%)96Hfype$l
zs?DfPT59;l70rhw^5{RnJtTwZO<ASR&3Tw}(4`i|=sxDHPHf~ZCqnb*EX*OfuKbJP
z=Ci-wXektf(`J>mebGS8#%#j7yov-;=xnww!vnI}i?*e-rb<ibQBRcuzeoA>A9w!e
z0GL$ys{Z}z-dxubS$Xt<>@9*sxDmm7V2>eKg%WM85Akr<V%8CEj4HE)SH>)M%CbAB
zp4HC@N-AP^Ubl(d_-Uhg-05z+nw0<wH`a3l?w6{z0(ldcQdCC>1bv@ljxMK$S2{Hd
zwa$YyjR~`sE;A*t`uQ0ac%xa<OsyWcTJ1Z}*66i87;@;eLO*cEe=V9K`L{rmvGKML
z01PR^c@oAdE=$__<}ELr(Hh(fhME#)`>Fmq(0XkgWi(5a=aYA6`c}pYKXGN|5w*8W
zY@ZdUU(S=|rDawaGrio!&uozK1%(EU<Hlb0PiwhSY{10XL3Y;p;eVvcS)gP1U4;E<
zX?;9zr0p}qT^PgW)biQ$B>1!_X1hr`N<a5sm#SCqJ3EDu0?4J+^rl)RF<}LosR%AE
z4}A>zqysLD$+y4m{Sp*}s*(}DdBolvi!W^x5{BZ?<0z(+FlvfAD%YLYa2q?n3Ih&)
zkDy+JooUH>$9HAv@hoh|dKhn&k3i=u(q7KQsF*+ebP0%A0mAZW^)5Vvxn7P(*Ec$A
z3$b{*Wz`xy1{3d7m!z?=j~uF>%21cE4O`_TU+5|JsZi5jB-?m*A1iS1t-2sqXY8H?
zEX|P>taq)H6jH%WC-48WELBt|=ja;Q-p$+3?_WOFG_WsF^O7<n-K?3d7J`zC6y5uj
z_=ilO**9|vI6bN!4k7wtewX4R$N~V=GjlkyICQT}8N=j(1Wr}K+yz@$0_hiWuCbf!
z7wzs=;;L%4(Z@;E&G2D*iZCl^Dj*^Z1-QI2<s-nxSNl!>`Lc{UQP(b|i&rQ^EBgu^
z#wJkx9YT{(DYyi^yD4?cZ;y*Xb6{bCRou0TZLC1IiM^C>q%GGAUq*5u%#fAaL0r|X
zUM`i(k>@;x5)oHT8SJjJm$LzFF!BeYm%$JMsyv$vKgkmee1Zv>ZK-jMt|Keu0m4mf
ziS@1`bQ&Qzru3u%=kPZJD$SOUY%tV}^UI@gfI{<gBtdWYMFEVkSUTfe-N|Da%2!G9
zce$`vBpO3sU8!xUd_f9q$!{n5Y`<EC#>&-`C#nT|iZ41?s3%A$OsD3WTW>j4or4pJ
zIwqa9?#3*!=Pc}ggRNG+kWAI)_B(Dv86?fm8D`wmFT}_NLcV0)sIuC9D9D0qtxLT;
zRK_xnnwCioyhMQagB%{o<HLGw)JIXt9U=XZ!5_0;_dW*<w*>t!APaWL`!WKM$J$7p
zC!*lty%4Oh38;J;(=a1h<}{Epf2Jy%ykB7NNJtDv7udL%44twa7dk(CtY6zl)1r|t
zu!aGy^?Ppx%1IE8H!ErL&k0L7E(nI(p$Aqk1##yWjrR<xlS0p{`p)7=DwBnFg2~|f
zB8NoytWp(<eDmO>!JsS$S%~Ied}V2O?$+5vQ|r9V?4IB;pU?PXGLg}2#B?=Tt?W0I
z??ux)+lWD>OLXvaehL6-1nML3%UPb~N^uGBY2i>iK!yX7aS`Hn6mFVVUmh3ZQ&Tfv
zM~3Z1@Oasi3}Bd_(wCE)2PLtQDsd}MYeQ<ZJU>{{3Y`MZI?Vo{N-qzz4{H}|A(px~
ziN8U)C13Sw_xvm+F@AA3G7Nr!Imh!l_{KF_9VN|XMUz{6@y)_nMKj1pD%p9=S9ToE
z{DU-SWKO%rz-yB_m{@QYq-X|c3f0o&IBKr)EEyerT^;Mf3%8bGd8oTIgH$U6gn?ei
zlrJXm(g!uwKPJ}zp1KMr(+WP=B&M*5KJGzat2NRxK*%s_5lOMO?Q1&B+hTUgx6FdH
zW@_j25HRyQIj9yZ!(SzI>O08ZA8m*g0m|C#Y;dv!5tdG7K$K`l;i}$=Eh7sk3>O{D
z8xOD=SpY|v($_8=ZD1E*2ZUq!ewr@|!tSpYEU-iF!(`SP#6!pk`;%~`0Ew}VHYAx{
zD8AD<OMQGU#G3Z^_N#Elt1;kU1WU0^>)*_w!-w;A*1OuckfcPR=~`KuJ)d!tD_6aR
zElh7l0yZE;lx$cle;$#_(@#=S50Vr_%N0Zm73me)Rt(3*d%J`6tHw!<dwho70Y+e0
zNJ|5ZM<H{gPvhZo2{eE5NzUBu-Hn%wskrH6^t1&$NHFWUsAE*yV_bEn|IX5^?78-5
z-as8i!`eb*PY5<oVNfMZ)qvwODL%3WO9lJ524C|Q!uQPf*zdd()k)u#3BX$_Yd%M0
zUTKzbTa5TpvyKuh4jl*C*bga7XeLyQMboUst!~L~QFGcy1}nim_a{{V$J}J@(g)3l
zMC+F<PHAT>R4^U^llF}juenc2PM1tw_8-KM5^Vy<nq565<gqY00G3sBJE9U>^3rKx
zuGzCwyC~_p0}nx7g)$F-Mnvyr$?=T;2vm&mR4ALg+hFJrgxu8I{~&UhS{%#i-~6>H
z40?dgkYZEu4lKM&vOCgSb#+`Hk0^$36!pM)`tIleE_|?*#xMb3Eo{5qj};hG7~Ph~
zfo>cq)iqt#as~xcNSfM5Ly`eQ1bG?s1-CKSvK+-u=!B=XlUMzXM$@)wrJcJ*cr!ma
zzpvU4Kq5ZI6$NPtDHO8I_GoJgkT(%ql)40m<_UjZY|A8Q@}dK?O{sIvWiKSFZ52ek
zQV|=>W`MYuuu9>;ing%Wk5pHezbwJkW|l8#t%Mh@@C^ZwxN%z78$>tJ_M*XoGb`Sk
zyC$c0!1?_4?Kbt_b8NXBo5lUON+)!>9|bIZ5)8l+1ZTfXB5RjyMo#NS*R7%@U0Ex1
zm8h;*C-`*+kxE1;5G@328wGd6d<%oiJ7f@mo(wDx=Y(lz0>hL^H|k0vNRlI&^R~D1
zT*p;10;F|}z6G(rV5%2SlbjBTN)t->Wu9GrFPZ=MB3nS&bR<M5*d7q_x;lz(YT`lH
zd%lnX#4TX>v&vI7dd2eESUs6*bkZ{1Fp#v!(^vJ8nxguT+%k1$OaPVeRgL#N;(J!d
zQO(&N@Ql9Bn-NG$=D;ks)^jo3OhoRaAP4^zG^`i(R*6#dk=mxv?Tl%4`^&J-X~y0O
z(pJb3bZpwWa+T067NX$gC3*THssOJa@l~f$B^x?9D63+p_0mW$B{GIAe!Y0iz@`0i
z?EGq7O6chJ@?b%3o!K_8aPEBQe>WN7fU-^Ju#W$-*6T2mw8+V*2n}S_zg?1&r%g4G
zZWa=vJQt$?r*VoHkhwVJ(=B0P*cGI?RtCISxN3g{27n0fl_FU1WUJjdc6d){P})PL
z+-cN)d#9>W8k->Q`PAbZ<94OMktS7U;>7!Q$~EHCzo*KXc`tQ0DJ||<tdWvjmX=fw
z9}*a4py6IlE`jF)NCW?wT;2IpKF)5_R-l2xlFYh#U5&=el_}3lG06n##}GUiSg=>!
z8K-`oKx_QK-ZMRq!V-+hQ;`r_1QwuA@~b&-?~Vnmx9s^dJX0eix}#SM8DCtAJ+)`8
zKv%iWc(Z0_^2gV$!8*T?X$a-8-Q9(a@4=-I^sFLKErj(%=O_bwAi9lXY~9=Ltv|#+
z$&WVn7^peJ2sE1;+!BsZh^%S~YH|yPQb@}Y%v8*^_F{Y#exx)kpZHryG<Q!n!f#VM
z9@{N{f*^+>oXL0ZM5U_03axc;n@-X2>eyaQGj1A^Jvv0V<_SMf<u4-5Zjox;KB@y@
zSsCDn6ne-0bW7V7D+UXWo+@`u=DDwy8wbTJrs)bDjQe~_mnRRUwiugi`8&6Y)@DuT
z1+R%DLWb+&C@aY(>(fh|Frk=D!!SXz@bjW_GcG8=y=f_d=I{7be{up3`|^2C1Xg5p
zDD?uh3~UbYwiK}XRsM}yLGx1&+>$L4OkFg`!sQ|YlaDAT8~!<nO{s%aYbNc_<)__7
zB+h$jQ%wYf`_owAeHH*|9!ojpzA-ErTr&wV_VG)-8_=^mSQ*0XLIP!Z-leylBpOwa
zx3wHIV9w<_Pt{NNhK_w_gkX(EB|gEObDyaVMln#Ujs4smAck_*s~zB{1a~?W%WAi&
zQ*!~7M<Wd<z{k+<p#fIxuoCO^FFS~}ncmTDS4AjBsV^S66Xs-fF3vsg74?kOm9#8E
z<s;CAe^7Pq*4QjG4<uO6sWodIjKBNvtyTurm)?^2t~?OzJ^r<P2L%9#p1Jj{{E*``
zILHL(VE%7Rb{vfRVl8ZUva44?@x7WndueG5YIp*0e?Q85$_=rDTw&bi%z9kZgK=qP
z%z~e$<Q=>Ru_7S430R7IB0j*e1_v*QRh*#3+Ru~sXV66cvx&T9Qrx=TIpJevfRy{j
zErG(N#~4)EA{0D(*tDC{^85EZAO*9|LunCX#Uf%WzG@a61r34~)O$#{=*pYZ<QFog
zbF?zBW(sex(?&G@SBGPOAS|59VZUc0Esiy=+7&Miu+NP{^i0AJppc&Al^+9lLy0m%
zeQ{e9$3nF)r_0jcz};R90DO}(Eg~vC*NM4yT2ecWTh8P31&`3?`YSbedZ5!GKsOM2
z>@qu&%*YJ%2KCq$dg){%k=k{`t_+aAfx$PQ>*KghrWPAaUg{)~Cv2%V-AGg&cu9fR
zX#k0Jw6Gx~rqkiiGpTEsa<!zs^(RGV=SRMq!|l<k*zmwzlZgh=tz)#eUN|a$N!hME
z#f!~+!*oiZiZ{zOFuK=H5ZFN1%3U3f55;RCVI?bTJ(~^!!FXi4k`7F_`F&?<S;Y3V
zm`>j_$dT!>__azp2eCxjzG~bZ!;m$@myb#UPv?ZB>@Z^9qpRVqOCCCgyELF#EGPsq
z)`*Qp*8!%<+4_E(tC7qlMjfOy216p(L?FHa3g7Y?%|*Y;fLzRE2RdP2S5N)eETa2z
zx@AJle7@QTJo=U#d=HU1wh-=G-WrVB*R_a`F(VUcdJQ)^S%fC!bP<pV7&uFGEV}dc
z@oSz<XUZANRUhkZ{o4AMRM#@d>}Ds-S_L0E%Acbl??Woj@P?nY%z*5~p&|Ob?fSO>
zJlBo#JB?#=K2rWus3|5!eE4T#bW_|r0fG;JSE!UN8k)<|ZYohnBwYCNN#ax^UJLP{
z>{=aPDaeV37?#l9272CR!5!>dv0)flCE60^m%`n)*B7Q9M@5c+)Y(h%eiac2w-DiT
z(3nHuVh}YZ8AYghy1__I3%V2&v53)@2&op?(%U*pz$Sl%g#{I`K`>*vm5@JalRco2
zQ7oPt_&I(fo8fOD%in_oU(2@DvP%{~B}2-)Ju_ulQDEt4pDqJJ0NB61`1dv5J&f?k
z@d`47<4-hMPwSs`QLf`5S>5_Z35c?#UJ;*O(BrO@>2}i9s0Bj$Oc;y@ypy3YzBT;Y
zBYW&5&HK8~pb~10NnRpb3Y`;u`BC}3basvge<?n88`@SKHMt`mHTtyMMKmzThCDm>
zkWjLQ7Gg?`($L?nluzVKbicvG`nyErZEVC&_l%9S&3%4Uog;JFVm}`3sF9tzb-Yf{
z3XQC-7sT*H@36<472_!9Bl@`4jW!~1loRUgjdnJM4rd3Qy_=u%e_V^<1U+)(?yKQ#
zV8NQLbue6-J>lt}UvAiuJ;@vl0u?G`GnUH8%xOosKp2P-WWz|UMSY-Go11i_|H@37
znVm>1zBf$Cl2bhKe{B?3y!z*7ZiE3an;UO9pSoaKxOw6Y7ZybKu5tzuA;{I@dN4M;
zEJo7|YiBpz6&iF)n6_3#mUU<gqYxgDAtdbs;auav3oGDkthD>P)?DV48Xt7#Tr0Eq
zK3#RbaGga5z!-NCQ90Vx_yr}Hh<+&PNYT!qpt$5ziK+7=U>`9oo}kgd?Z~wufhU-;
z#$qj;*cGlDcP6Aj1ub8-ZO{Bbt8oABz$oTTN4ijYKek6j?E)8H>HmH8j`lxyZ&%&n
zl1hjub!=5XXqIXm1oM<}5hbE?)DGQc;r?Rvu?28h(KhFhRMyvMO`dRsL;}TfYjPgO
zQk9*nr^M591op4u>-4O+grUL^!5`+5MtC)M(LPut94T=8?$~@hyGce5^DDfFUSMg5
z_yg&Gv0JczamcHC85ikPTyN&I;sKNS=EFBsXt$<3RmHb=&iKj-{2d++=(a9FQ7z3s
z<4L#_+OM;?F@Z}(cKyP!6&(jz9<p5A$`Xe74s^8hCGKP=+Nq~y{P7AFQg%Yg!U}d+
zeNe>LV`C=cZ*hqR+lhV`E*uo4eI7qa3nO$otqT<e`?Kth*%I<aA-aGF=pY3E{Vk|w
z;d-7AuBfIv3OI5S!7eGaDVpon4N$&BrP+ZUo3E0`AB+(|kc6pEw9=Ekjtev(^~a}3
zjn{T8-y!R#fvSxIISs@g1o<Qe<Yvba;ESN<*qBW&o=Y<R9W9waVsi+;i)%WsVDa<y
zw6DanwBW^f-_ar5-)0aqFjnvaM%oK#6t3NJW&2CHrk5lui2wcjQ+vQ7;OKs224f_X
zh*na`pm9Oi@zGawPqwKE@dAxe2N~>jPF+`Ugs5~`F}`}EqmWoIE>}fG&?WEf8F%ld
zd*M1~KO@TJBVMC<)w>w&tTji@$Y7Z4E}w3qScP;W2t=4>=dy(+7y(3p$e<$Uy#6!T
zFxFNuH0sB4G7Lt(#osFDk@!yMY*&0^oLCcvVX-6-*YtrGOs%vU50C{x8<pQZ$>OM7
zX|}xOPd7XAL9j-EmSjK0rKsfWTbtRo`n)efEEV@Sa(%pOMqpc8Qtr}*EH}1nnsUJe
z#6vR_F>r4QGZ(O)80ApAHPMg-;yf%H_36s4{Za5~*-Z)DPWZPsTVefLukh45wqsB>
ztaGN2;}FU!(9d=PiD=MeqTkBBHHk}mIs!5i-A&0~8F8!CyXdB?TjQ&In-jZ(P)Hf6
z?D5dbu8+K!TjHrQj-`c*JA#Lov?{Ay_u%jPlFteeI_8#C2J^o}sXhZIUEVe%d|Odr
zG)LY^RDUHr4IKi=nENOiIx@@A%7Z*L!GeP!mc7iR&BuPK0@z_dFM0eF`gCFH+J6EB
z3KguW4mGZ2{$lcAqoIv~jDBCt7dC`6kex)uh<C~z04%J6V|g-Qr}Jw+3dI>7ZyhP2
zO-B=4T0nu?moahciS?%g!a0LJ+)_e_t)CYb$jS;IG8oN0kJ|JBO~7<gqC)m6`^Vmo
zI+Dv&6gdlIDe#k1CAoML6>K9eFjYhx9Y!I~R3I8@aMtC(d{63#!R`&V?^Pn7y<bAH
z7Is}v-_DT%0|eR^j~f-Is*P?bflZ@QL#H%&0IoonUEy~Ui@*Ie(f3(baxr;ZZHQn6
zotx%?$r!Sgne`B}I!?FKS?_wpi{id3BqfW&uH6Qlau?C|%Qr=!uRf1Q2v3Rd(=H@D
zPUmV2Mles~+~vNiL;0Cj4TsYewV98w8KG}fn%FkXM7S3^(#=}<9hJ3HeNag%?7cO4
z)`WzAA9wB|u0!*dp0s#W2vww!+%8{G<D09^D+Ar;1Ke@VxXgwG-cTTdgcbd`4HeTl
z=32`GbZ6Hwu>NR?UG~50f<wGEIy^+^ws-rY+$E-_)nqi24_{I{+;bF?LR=qwDMesm
zlwZTHhs<L+e9ZAF&N`VmwTK@F5OiQcdjbAr@ESoq(3TcVSr{6|jT&Kvwde+B(!7Zp
zMwRRm;K*#q+~bH^F<7wB#si7}{x&YFmjecyX@i4EgSahQnJR-m)kTMWbt>pK6Ficl
zkc5VP=?%+;`jLkD<VpBNTsQp9m1*9!uiY^ZsTIa-%=dM(4wWB36%@Bu01aYsO0J5w
zhe}gv&sWiB!85ZBRaqa9poe(q)#Z6ZS^`5~&#Ahtl4Tp511NwyiuJh3e7_OuDM_J-
z%S7Ne0}X|HtF2<To+;t68hTJTZQr)siz7d<h^6eGF-}I6V?j6SeJdI8HsqK~Hzz&!
zQI~~ypz>|WS8}naN?>nH>CqMW`;LMKSzyEW5DdA%AtZjyaGF1HW<*U*@!8&J!LJO}
zo^Ho7!baoIp(o^`$8R#EF2sn2d)pUU@jHK%yn?92WV~e_X$^5Zg~mUwvM+{Wdv>7V
z#XWeuH-ADkHSeyM9(yM(s!?8p^rm$C-F36OV$doC*ixPBnD$iWOgav6%5bwEqer_M
zx1L3kzyLtnrma=dC-h<TPMLQYv&>jA$0a{TPaQsJ(W%^*<<+cL(uZZ}nwR>!hag=H
z@u#w@%f!u&%^y)7sn);IDxk^QN_*KNL`Taj(PC-akS<l3U``ht#k-ol_A7aD+8{!V
zJra_X96RlHi}GmNhZfh3GTQQS9Tc!EIgni55xRKcz*=v6SthP&mB-tQ*~57m%0D0x
znVHeN&nDn>PnlTL<(4gZf`A79_Vr{LU+|L9apgp3wBYyjgbDg6fjqGu343JQi3J&w
zL=9eUN}1_*J0iDiIlYj%4PgqjQYer^5vZg>=7uVp*e5))+yX!N=2odc(29MP?(c9A
z&TR-gQO!Gs6jD;3tLFlZ=ZUYMt%;}S&%3?{mLKLkAnA>uzJimfw-WuKVhST`7A03p
zDKI+5A6qJE|KW!1*Hh%9x+i85muGJ3k@{@g1dGFLru<vvauJ#~ra_$YUdpirO^a~9
zJ!W8!H&!ny!~B3w7TnT#53q$ZS~i>=P_m$=c?ZeeFJ)SroI*#7<JLu~8a~O!{iHRo
z1L;?Mg8=0LvCH_Nqkh*c10gH-3B?)BY#%CvhT#N$jyG1w9BU0WP8S#G^YuM>pX=Ld
z6ca33?s^aj22LOzi$M!-KrMOR!{=9trYS+mKnx;~9nro<hhM>LiAr5X@cwmJ6)SP=
z);Fw;d7|i9ZMQwtemhfQHJ_)6`16w+d5vDaliM3GZVLUM27XWx5Qnb9aaWNJ*JMLh
zIYWb`#iPtfn6sSyMB2Ofa_&|4utjdO{uJnd&UufHA954D6y@%$!CNkchqj_*7|D&k
zyucrN-a1p47GAn;R>r2w-M)(JhbEa9;&@ojZU&sOg(9RqDv(t;bY#>tJG^@i97Ad*
zeQZP1Qp>ThKM%k8LPR<9KxX^b6&Rjv2wT=Mr>pL!!`WQP7)(G*pc07SaZUq^z)F?V
z)a*NM1_e4BbJp*)1b<Z+T;Yk{E+yvOK~)Bn&<>BK&38#yCnW^72hasJ%%Q&uK{}(|
zhi~XRmw`T~Iu^;rW!m}6y53W7kQ~c#1uPR%iBTvoe-4gYSj%OyVwDys(X|Gz!#2AZ
zdCzqL39FIhr?;mla71_5QQV#?px7$Wc~=1+W&~E5SU7w{UDnL3K!p~r$uvn1j+nB=
zb8V27fOThE6!Vb)d7t_C3H&}?uxk-(*Ff!*i@<I}II`ljyF}t)3F^Ues%kt?P@;wA
z*H(-$3lz8E8hW;MhjgSTF`rSJrXmI90Uc1uYj<fmkfX05=JsiKY$7R={@N=pQkJ`H
zRHxL&V>Lo!RD7pHLyb1aIvy?*b+p(l2j=rkw9_bU+`$fUir-Fe1C>g<suW`%<gW}Q
z+1}a+<{#Uj1uzlVAT#RSK~eHWJ@zKeS2pQnB;pk*4tvZI{*wDzoD)R>hi<@*`(QxU
z&<#AblwA$JhY%h8zUpw+51MnjnL6zdV$rgH|9ni<0cux^j30`BTzis@Ph8BkhhfS`
zt)g5Ml1eajbx*Y#-6Wa^4Dib3?6`;Rs4}Z^_pdQ4j{QCT(c20OwIkLL*z(SN^|VP|
zLIpg?<Uw@KWMB;tA+8X<VA?DGbK-EelKk5FS=vR(*Xs2**p6?I_N}FZ8bfvhHN*By
zDqNreLi3G;`K0QD0Y_FRHI{D8Bw?>gftAE&Nhd(lQsiRA)|JDb#s|cV=d%5#`;VcD
zI(QB?Sm>}*aff$3d%TvZNftmxp%|_?EXK1neR3!aEy(Fy6Knxn0S9+6XdVM>^Z>FU
zbNfkuj4@S$+vl+QpEYYVf}2Nx2v#`Sey~amS6SZ?e9;~NXE+Z6>`KUwdABEKuMO^~
z?6U90-sM5muJrV&aNHtUm4^xgw*w0Mw<PL(t6qDVx$LN=V=&N#9|wLJBRBhuNI=}7
ztN!irfG*`SuM=HD9YWc?Xo^cjsr~^<PB!M<g_H)!3y0$nJyv*r%@o5p!N*TX*bXuZ
zQ$$-_O7e78rzdWDlQbs61LCjfdYXJA4pz$CleOo>Ln{z2TrI5Dsv4L{sK#y*2_Vq|
z`-qBAv4m5H*PM(~qa*Ux1nZBQ>0A^+o6s0<2#UKE8)jRM@t8|FGiJ%)<CuPktUxV@
zN5*@QZFz#m0$g%CjdbcZ?2;U1)hwags(A`&ER%y1E3G3eSuA*+M@Lzx`v|(C27Oo6
zg6!O*dH|5Wo2RM1Ki5|K29taUdX*iC9}t%%ohIE8s8sSdPzY!k>EC}mY1FwDWK3i%
zcL4(g5<DL@)t3Qekf3|l<Tnt@C+?ox6Vuc6M}N-HIW%#pMejXHVUgI;yY7Q;K)NVc
z2eog(an@@ZQU_4~w5J@nayRCRQ(OH&jqJiIrqvTLCMU(2Qj4pk*5Haqg>L={yK%qo
zUWx~f{(FA2=O52(5nEDtJx+|e))|MJV^i`~WDV=sD3Gq9Ru%bEA(Zd~5)5oCQ$hXt
z0;wJoaoHR7Ai=&FH>2*dJX<98GefP+5i}pUsfwfzN6}3v4N8Jo50v)$X#!NDzE&ZX
zoQ1EMTsiO98}@i)LnZaf#_CfBN)I*cVeOCt<SlTN6D77XQj*=f7vqH~gR;T5qYc{P
zvIKxP=|)yP?=%{PIShmzHje;dY3*OE9u5~jQ?DI2Thw9Q6tG#HU1bKUr8Ev;3Oy+A
zkDFSPZi#S`VWC=#eW&jJqYmgRVt%G|wluI?V4q$0LC1-2ll1f;VTWS36-w}8aWti9
zi6sqtHH1#IjxA@f%4YWvjwBQ|3z}NB(knMo46}B2VO>$<yul!2TAjpoV<RY)i9hH7
zBs<W<nXXoL8n&}vJeUWC<2rGda!}-p91EO%%|l}$^Y&(-n9;^dr+jOhz<qyXHi{?X
zFe<(fKFfEV^tY<m3-RFHzb+K3e@wK@SVvi{c~(MPWgq5o19hj$Sv0{$&for1Z&}_8
zO^e81EvqP3<{G-&=A->}t(QD-QQOf&2nZZ@J86l<Clfvd`NL5(45gjn8zsUFL;<IZ
z%i~zZ)z=!|Qbv;Y2o_ObvbJ`c(uo4gKC6gzGL(EI5NSj=JSHw85T<{P#m7K0#k6rW
z0q|SfvtWY(Y`*H+*Yha*hze)s@OM6O@oNcWY2OZZ^=qGt1p!;zMY6iUST|d^f_-y;
z+(5PR4h}Uv9DF-P+NXKy^th~Qvb$>aoU+qce@dPl-D=&wg-S<91UB<LgM;l#!XlAG
zm38_3qcY-(xo(cv-5zjlsATcC6=y^C(1W1M?kuiM<Ergr>WUe0L;V;-iL_nQuGJig
zJF9amn~cPIGfW&iScgxZrGFsU=5!t0cXLr@8r&06%F?%WlX8Zn+QILO$S13dLZUHA
z!S7_#?Q;M`KXn)m@l>Y;EbZ7AeTKMuP%q>q2=&n&Vib}yMI(-ScDyNDae4R$cFg+7
zHqL`V(%Y(Q@{J9(<(P|-|EVU~U@@D%iO!s4@Xz5nMT7Vs@%_OL@t%}YGQrBBrurJr
zK8R`Hx=<d|iQY=7IZw6zy%6z0zVits-dP}?N^Ll3^JUN8rnU>RW7>G;y&JsRxh&fF
zM%dEN*D@LRrZE>XDrsH9JmLA$zB`JB;PG;WqrS``%&!xI<(3?irJlHqFma0Z0^@`N
z2V->Q*Xe}Xot?@PMB!&YHw@4Sfaao=jweyiuR>K&z2;71D_bexehtgUS{DCO)t#-0
zBSQ0>)g?r)%;O%I37S3!AlviTNci`80RbLUJL8`MV<6b%uodj6cEJi2kTUG3=A6Qi
zkfEHynNQ4|#V4LCOw`TGUziUKY!?NK59w6G_9^>;)O=A_R#HVq!mgV~pjCVDk0A6#
zAB-;R!uB^2?eE^i;w+`7zXmIsXqd<pKo=h|6|$-wZqz1t@>IORudP)g3M)5`YkaZG
zw6flA0E17`k=>fi*3%Q#43}K^->}V0QF&H!&B%n9lWk>o^~jc3`&QMkZT71c%9ga;
z63i{0Y)~DhLsN`!njr#t$LB>sNoiTJZ%k;Xa~#zo#Z<`zv+VAHgmqH!F)4>*2h%8C
zmkxqcPF{ksWG@e2ObGO|_@O0!A%c4*@yjKn)H**5XP&>p%1G0_oRtIyO-Wz?hNRMo
z9W{L+v!@|UJZ2;iNk)(lQgbHS=mdTqT{{+fFn^^Y9Q&?E*af4wwiI;@FXwHSQ37z*
zKLc7(HWx<p0P9V}x)${D_R3BB=cKm;`jZ6U!3Inml(>dsCTu`A>o)!FGtm;Scm4>1
z`O$zipH$<sO)n;Cnb+3k!)|`$o@-pQc*>Sf985$tVk6jiLE0;B17dkDdDOdK{56<o
zl|mDTS>l9w<BRbpx$KhdUH((=(Q}x#+C>tk;G>agcSY&Av$U-xKNu<{ZzfNLkg!l@
z!25`!DCR1HscDUx?asXvErXni<z2K1nieGYJlh;-4~(i9;YsiWkiRlxT(zy8S0xH&
zzgyi@DakaCnAsmk7Zb7$+eWyGAXOYc^TYZLADl(VaIV3=pvdVZ+$#&jPt=N5_guw?
z9<njY76TWpH!&VC!sGMr7kfGZLr=`j*qARc0XKfU4vS4|>g5boJaEe}E$3UOkE}@?
z$DI1w0h9PPr>Mc*;7Ojx@3E!T10B$|Xr6W$s-ap_K45Nn3eMQMh-&|A8<QN=(`{i4
z95H{?LdW!*BO*voI*TOSVkJa*FVR{d1FEc~+%#tS04{E6(#|F0k{_@SH?dbMkXcUa
zIhnU7Uo$**%DWFQXzP+D22&OmW*9_Z(ud;tRuNG^XFmshfJuoN7^C<xl&##qc=O7@
z(158=pk5~h{Y{RQVp)WANO^S}DBA&uA+P*l_mFof^b5!fp?p|Kfa@WEJv{aC77yYv
zd!00asdI${A$Y<q*B>leFFP2v0<&8VWfw-JdpK#Wil=M9PGA+3(!mH%>w~T>XW_D&
z*YMcX#f8CfKtp^6p-%pM-6kKf9&4Oj@;CDz_3^7J8eqIe>fiWf$$wBrsy{!bSpr|t
z7wp6W;VVFG<IO`&(yhWs3zv;TX%ToNOT_~?wr2OaF&`T&{=nit&j9X(0k#7Pk{+i9
zpA0KTyj#!6<v#*K7l8JyBA0qm!pGr|TOJdWDOUYdO&~bI3%X+o6_#oI>m;#Xj~WOk
zzAd@6llqHu1*k7ETP|#Zi4510A7Y*A8M}Jxe{*hObU=`kkjDYG0P^Tr)%&Q>T9^34
z%orB*E!?}zZ7(ruy@esW8<cziSQ-F8dY5Lkr^vMF>_OhF%KH-5+)5={7rDFb#5RWA
zaO9E#J*gz}hQNy8OSs5|Dnxb|(d4Z#mDf34YjOsawRm9_M8KDu@&>{y^6C5dw>05C
z!-^GE!|0C{B!>OUNQO=JdW_uy*-Yx+=Yv_<iiemALIVGxL2xYauarw^_-l`&fvR3&
zX3ATA>xYd>3;C}c0VFxU1w?sHn<Z}UqiH+>R?c;Y{u#t$sCcvj!I*%puqbD;gME+m
zP0{l^MIs(CIR#l%;l~q|zd)NyhAU<Q-xy}cu;#Qvj3Mwn=#Sgjqw&zbi<EljIapVc
z5JBp>+lc@tfO<gKzzca}c5%T!d0zL+NeC^R8k%cWr?ZVp#_2oHLX8jJ2t}XG5?~>t
zp{W@-%<HD5#1&cRKqtdYzcXbNdi<!hJq|#d@_-S5Dd;7wlwCb-WaeGyULa(=XA1p3
zA@MeMnFo(E(3-YHAE5&CG4H8nY6T2fS|2gX^6tW;GjQI?emzy$0H&|T$Mr4yCJ4XL
zuI1U6^A4o#VRV5>#}RAK<Fn8d9PF)^WNv@RR%H!aaxlUUzt-^7D}Jj4+gYFaEwVzL
zgqSIv@cofwcOZR9+V?4wkxD#rZ;|-b%eNG`%2N}^OEyf_(F1}-4>8I{J(=g_T$>Ze
z`VGcw+4_M!l%lAr!fo;XN_Y>O)PO;s0@2L2#MJ8E)+%euQz*M`A(n5D@vL=>78F47
zK;nL6+iH`!Oel^(cnz0;->*C$6=o!vz)rl<<51}*uf}Ps*lX@KUkj-K!X(_U654Tc
z4&~031G{_wd3=0~Oq>ab+{gjAKP2v(=8tjsor-hF#Zc7UHU{tM7PaOfBgn~*MOPNQ
z+pLYjS_IowoLqee0%lRSy{eZ(H?ThcCo%{BkH}meDu9U<u@8bk;A!T3)m*O+GXu?x
zVL!t!$oHw?{l|bOAaO-IK`La|TD=bYP^Qb~g}=qS6U7@Y;Ge7=?0@2XSy=$8e;xz?
sV7Mg!$UpQ?$_{|_@A@aVFZi#B{~z;@D**bxD9~d8@c-fe>hl5q9|R?Wr2qf`

literal 0
HcmV?d00001

diff --git a/public/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/appendix-dsf-30.avif b/public/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/appendix-dsf-30.avif
new file mode 100644
index 0000000000000000000000000000000000000000..555f6fb2042f5d5aed04f2979af7f4b233646ccc
GIT binary patch
literal 16131
zcmZAeV{|UT)&&a3wr$(Cv12DsY}>Z&9oxx{lO5Z(ZQIG+=bZ2T?ijbm=w5TpRjaCd
z{pqe6HGzPD@XTC19Sq$p&4B(jTT3(M|6p57!+$<-iLI%N;eYsljo8A(#_4|$2*|<G
z*yaE5|5*u38++sbIATITpg_R?LBM1Gvp_(6l|Uf>VFaB2j{y?-*Xut_^Z%cb|L4kC
zTH5{hQT`i6|L2(gH}T(ihHgyE|H=4&$$ydmFA4mgJaLczy$*tdrHk!<QvN$douz}5
z!#^Bl>0oUC&%znHF^dL*!Tl$WX6fu;`X2xU1oH5&`}seB<Y4Jx`5y)p3JU6<`TzCD
z`w#VR<p0Hh{}bUdboCJZ5AEt?$7AbYYX0Aw2#>L)u>+5@r?ZQxtuxQRK9)9?hEAS5
zhBgis|1B%NrGxGN)fWT^7&H(B6bJ+?1Oz<n-&j<ZPNx5>#J}nOCENU~An<=*1i{$E
z+4#RT{#O-#5nB^O7Ze~E7;FkgMp-d1HXJ0DOwV5t9v((GGm~(@x*QX!^2Q68Lbo8@
zDC(DKs4ne}D}FhaF>TyMEE;fklm(@D%W>V7e!nln<SKjpc|d-Q`Qx7LUQD{AI$XTB
zKAwn<b=PoD>?x{BL?rpP;G~t^-v~DZdBdSMbJKNChtv@F0I9iNE)ZX`@~)?s#D6@a
z1$M$3fP9lO7B@}$zj_UP8$R45pRCU3xvFsk)$P>ODS;D4MhQ&6Cixf&)DmC!kRrvz
z$ik;<joNnJ2MKVAXBxH3_g=E=A6+{--@b_cx=(N0?W%^D9JzO#l*YLC@GC@saX<cb
zR?{P!KDThvST&jGurpY+%?x7+Tru8WMGFCAmHbIBaq@aWL&78r$y*+Q4^Ziqi_?md
z<z$+3X+I8wEy>OH1nS*(nmsHvgC3QHSm=^a8Gz&4u@6vo^b7SF{ccu#e%@~y&M22g
zf@yq#JG*RL$l;Q8+4d*<!U5LvtsOgKb^-B9i~pzt=RJe$vnho?=7dj9o1w68o9i)H
zumx#E<F>iIVjGBc=9l@+5OgK`)LC+IxL}e3DUr=MHA8Br<U^vh+67sA&B6#9qn7_9
zxS$%q_gOd}{>X6V3~RX7v*rz!ry?1V0uPe!>03!x14MIu3Rsq`;%4kmpaGDh3}@DD
z_*b}kv9g!~sgftC8e`tqv$yD{q;ok``emtmyw5a4a-I56<xHdIQtywK+hG!EW)rFV
zQ<)8PUJn}p&vlqTM3?r3_uH|cc0Uh;vp9_8r|ihJSOfd@4u_8~EKjkuj+GuR?SWFe
zWjOmH7i6@U{si!9<4SxR)M|3gi2Ox$DbH*pT`)#b4EgC1LIf1VH0~cf-;=q+i<4Oq
ze$5InJY;tR&C8V-O6S_x6p4-F9tZdhP+i-19r&R)^1oD&T?b1fM@Rl#FBhYnl2~T6
zTdQhYeQAcc_9IMtZCKWgHb$lTtB%U(**oM>Uqd)%I-?K0YIIjmjjmYFG@5WXKwlwE
zirJ(709}3++cg}OOPm?yfe@DwqMyq!5melG+48?M1_IV2)5AOq{oNh@mJ5Tho*@{M
zhG08I@}v?b3p-79iwHqNr!I%AUYTt>;?QyN(r=&XQUWfTOu>=YW+Hm<sR-W~u!I)y
zPwq@MLFWH<6iLK#C`Y2b?5|MfU52v}f7;(N#FJJR44vVsdm63|!0JVyAZqn-vlR$3
zXPidv$DkVBAE9Stwu?>iBC;|d>Ge0EQMrxf;tbdh3@xR(?rg*}&YGHDc-RoaPkhO?
z`KiPd=6B<Xy<+eINq4c`7@-ENE%9Xd<r6mzU=k7GIyN$YF|*{UxWaav1b{;Ut>m7U
z2`Ybn(?`O@Sy6IWwau~gAM^B9A*9vB%1l9mS@+cw$*^yN#^g@F0n9dj3-<>6nZ6yt
zDJiW=dt3%&8w8odiDAzGnJP?9!iWx4*|?O){#o>=fV0MV>JT;4SNI-%DF*!}Yyu}7
zUqc{Q@1)*EK@^?+0)eRNQ=z~(6M=ilqW3|fd)f6z(zt8r%5+AKlJN4DA*2*hko+Re
z6x;heIrG*|M9N!Cqbzwb!bcVbV@1Dvx(f=N{6-ePuG?zONTwbT)-pKK0woi_7IpaC
z+ln~^HWi*VzRo?m;ewf+2+9}4fM`$Iq?XTF!6q|t3k4g!0fRbs(CLC>lRVLoz~R31
zhD0N85S2?GM!zNxUM0Ol=HIJMhHTw8&vLf_YZ89Wf?YU9DC4ua2Vt^1(N5DdYHcq=
zRO6s$aFRE_MH}Z?8?f>)_-pjqK&|`|0xZowHVT%ATnN=NQZ_{`-H3Ppx;XSXnJaiq
zhKi#%7#?OUV8v<%82iaUHIQb78a(VfIi31ki;Jh5r4Lg;uc=;4O=$z!Hd39M6Diot
z;zlx@JF{}7Xkm@<NtpXIvfPxQeVvHA3^Z9wJI(Z;m&c(b8_tA1O`bJ7J3$^qfQL9w
z$r0rgT(NnhjjvUppPn7*391Vt?<+1*tBoMYB;0tKCSkwd1dh+@sD=<cvbjyLgDir^
z9e1Em=Lpx-rWNjh;VI+)g36t_1zDW{ca2@(ata3qHwqnTF0%B`J1FdN{=J{@Fy6EZ
z5^Z8{5>X&)_js5ki=Sx6k3!YxM7%53cRnLRsCfYxuF_OE4Bjo!P+^!mRt9?F`?Of2
z$-`otAJ09?(T)36P*vl#ZywQeM3Y--xr)SL2rdwp2GK1kbeo=-Wkz><7T#NH5~vGe
zLWuEkAtI9?l)Cftkihbo()Ho*g3~SCG$+(q%cbXtkNbQE+w7R;cPD7IImnI=QIK_i
z8s7iflCNYlgrN|Wo^Y~wIOOF(nym<2Ru@<qY-P2QU_!+#Obe)|6iDJ@G2$f0a1UfI
ziA>z=ST&Ricq4G$$SpO<S6Sl%D;XfC&FAAM^d55Ov%=7{UDW1Z7h{NS>h-A}HWfr`
zc6?+?V$|;_-3@m#XZvB6`Exf;R4)hwRCeI84-5km=G5CCc3=vKb&bwZnSif2Nm@(J
zfWpQvFj#c7v*uYNWn!hP>OU<>CunljRCu!J^C*^nD{$=qa@`9=Iplka*|;~Wclzjt
zt6<6B;N%8A$X^k-{{2-zZcd6>*(Q}1GmVuUxcm(lN5@V@1E*@C8g6&TR5At0O<)f$
zPAmVxEdyeq$G>c{cRVF;aHTwLfbUUe8E(wx`4Z;Qz>6b80X&}?DCEOT=G)Jrl_RE~
zfo+m^TE+dake=ka<=TuKSk1{$+8#=@!8<7>oYNf1HNi_V53Bbu^^9s<#@eolKH#Vz
zoD^iO`ATg?Qh#ztg@d8G&Us&qJ3WR-OUdcYgOM2+!ol}9zd2NsK9kq>hAu!K)@-0K
zbxI=MC`<^`){z>2MgF6JP89G9r*fiH=t?~o(`c?bRf9LwIx3#9_AOc`3pLFd|KQ{|
z4%lCmrZcELKGAfoMata{aah}HI>I1ZqFRKb!u=ZF#g*dF-$%Ne296VhynPIm9^|jT
zYpQPVfw2+=_Ex4IOHmDdG#E?w;h1I0kH_}@Rv+AKAmHRIzNjppm|Uh7nq^<|SyTLZ
zzh(Z^cGOUcAXOm}EHecQI!xre^TUsIq`P9^+56st9OEp<v7_Hf>C>Wut{s->nS>@k
z$5XzMr*(Yhis=q(C{~13KxUSc<*VgIQRE?N&xvL-z4Z?xOna$WOk;kQDGH_56W<Al
z1Sv~|GW6CH(VQ7C-@Y+&auE6+E?mJJy&IidRsK@<owRPMZH$VZ+{U0Y{P+m%xc<Up
z$<D2;a-22Xl(@}UsmHaMgf+voB=Uvlu-@jAW}b0`f$|MMGt9YI{Zok1G%8B0fSQ=n
zp9SA*K+C_qZKvrX7g7J~Csz|{{<nY7#h6`$mj&`c_%Py+x20yLfRTN&em#MWmdhpc
z!<MbnPPr{i-QtP9s@YoEENv3jfDl%-9;)@M_@gh{a6~dhh`Q%QzhuoHThNTB&{bK?
z^KvBCmS}QNlRK`P2{~^iyULFCy@CK}dUBTlIeS3sn_>hC<7IHMrT~&)zCa(IIqh69
z-5-esTtU6!B7m<M>If>dQf1|=!kJFXg(#p7545cRGr+8jqR3Nx(l;v2JkB0U-t(U2
z{y5{XVqlfh=N(}YoP&nNK*Uz~OrZFN3z1id{IWh*Q|Ik1J!ueBGDEe;GH+46wI6&8
zaWD>RcW>;4`PLoB?XJT8NNp<~uxw^=V6t;>wW!syW&<E0kq#nmPs%i?%fxa=K;LfH
z4H&W|2%!=Lr<mkTEP0+e4-&y3GwWrRf-9!raj#@o4cjK=OKy)xYnrG<d!Z!XO{{CR
z7>c|Lgx?G5$2PmzKa`m`mN3L!FDc;$sn9fv(P+h4kbkU0HxJOJ;UVtvv}GQJGpFan
zkBa{@NuMgoBLrL0+RhAn9RhVfA1y^1r5NnJ5^dfaL89O0T^q{QM$y%$4udiL`Umq-
z^lywoFEu4#n3Xm1Q8fYgd><ytbJ+etEt3h_qjevEOu(A-n2MD@QV-)Onwg|4fkf*%
zgGPAbTPf}|g%f+)ovbv@wM4!MN_uQ_PP>e8Z;YHjGG072Z^t-eLZ~4Ad6)8#3E4Vx
zpi?$y8`Ei=0Kz`zPg00c-8#$cUtY_KS95z#{o=I;f5V09PIDfm99eSl2d6jOBCq-$
z(*asZz+LM}&$<?dl1=~q7seFzI6b<sIqG70k!Om)oqF~XZIpovx>Q-kD4Jw;Cc`T5
zWK&#E*D;R-o`iVq^jeY2@v2VxJvq-8pJ9{QSQ0|+r*$;jEelZj+bTwB{ng9iz%yqr
zCFl=oyggpxLu5RSsyptN!UUP~-?1^0kliU5T!{&8pR7eGmNU!j+zo`}H`aSDg8&PV
zpFt%79wo4hzoz+ydR#(-#hjbr+DLHlQUkYbLi18)QJ7JQ3Pds4K7Whs@O}$F7=lDX
zEa37N@*fy4x*N>1d~aV}%xz$0`1;mIvB`m75!JYLnXO~Kt1fL3v4$oR&C)w`DYQ+J
zg*cl><&mq-OG!d_IjY_GmvIgG_HUwr9ACKaeQsK^<-97(i(-EwnMDLBYQw9Jqw>$%
z)yC6?9XN6u&+mMMZFOgG3I)x~2{1KIH51Nmc=s2ZGd|sK@DEq}9EKNGUOZC)`$gQ~
zOYkM=A`W$qYo&QEHADs^=?8ghF~O#>Sz0s@PG7Ce8WJJrYrfjh2m3jex+Co#_SCq(
z<?|YL*kb!Up(Y0y#tJ7C>Uw4p)wZZZng_ltOOZ_zs^Dn#QW!26m=}}q%|PM69=9(w
zFk3!2b$o)Y3vCYez<k)}SSh_A$RM%=MpvQl5iep=3GSW!q0FJdC1c?le9ve&Cw_rj
zl~@LO@VTPK3kmSz#O`hj`=etnv;}l^k>)yXU$jg86iqg;5sHf>IT;t!PRI%mh<h@i
zT}Y3zt;OY3y<w00DXDV%`DF;`E~iOmhQdO92_{?KgbupWVDH1Q$C`RXCAx1!SOrCJ
zq>p;fKV4kJ<gS84#T(O`UwU<_qpHjC4Lm%tPYRjy=I|Y|2PTpry<##=Ae?-qmwvST
zSq-3G=*D_Yn6Y9c?_xnt>mDoL=>g<0=liQ<ScDj*$iI*3=iUG*Znt@x2+{W4Hqsmd
zd9y2TElas6E31B+=KgAM#4H<;zs;-H%!OE$<2Q570lssQUR4EUNsC{);ng4z_g2z`
z**s?wCa8Q@M`bes4atX&_k^VEpVyMB;=J0G5<6iBOWp7dE6KRkvfTO=JhflO)(zWv
ziq@OSmrX<$!T)hRCiSemq-@>8$nWn1Pn7ZdSULG6>}-(i2S8o~PYjEw&j`%H1rTd?
z+NR$2`Q%%$k(wdA^1Bc2x+F&p4PriSZ!Eb*gFgMKS8mR8gWhu1LVK7AK+z-%0;ZiK
z=Q{=A(TfKSuMIw;;2)W}&ZzAY8}qdd;*&hSLyCr6Qn2bhcMr0j(+ySXQMRef#^ngz
z3>mzoM!^Ti>uVl^G8ebs;=H39^us?TZUWMa`dGchNRde6!ilS*kXqe*^9L4QeM@7u
zn+V)O{_F-f0D%nD>(vmO({p^@FA^jEj(LV-ASwZEM(6olNi!0E4yt|WK(G|?>4m5Y
zMuIn2jtpB(p?kLb`DWSh&XfhUP4c7)@MyLvfWTu9Yo{Hyq(QhH*N7Id9u;K4YDPBi
z_rq5LCng}UB1KD_dUWf#SiDl>qIarNJx1JOuBtZL3VlZw$t(Z62Uff})iCr>yPEim
zIm^B1H_n9Hkza#R(OPn|8W9an4=L4c5u2y}2%a!=+sO5izO4WP5_eLk&k|{fbOF_1
z)zV)vvYjSXBEll@W4<+0?rH_yjVQ%$q4iEJLp-xo?meY$z%?Un>YP@ohmPje@dtG`
ze11VE8a^UlK>1u63>9U#LF3Fs7a9Vx#a8L;Sc>vU8}U##0{)~`HKroD<OQ^%4C|(}
zdE+H-0_hs{eB|EMhU0GYpD13*+m&r=9;bCB5-k)xogh$Fj3qt8>ApD;DlEEly`smU
zs(^V-{l&th0TjNt$3Jrs3Xz6CC~x&#NrGxJ)1*SKe)Z}q5L!8-Tprca?szGcrw*u$
z%J`^2RF$1@&WRrS;FkFaBctLDmYx#$x|5jg6!M*Tn}`fwu2_5i(lGES-fBNLLfr>O
zIP>wM<lR7^@%7yN&Jnct{ig~H8Q~=b$WM;5I=K|OrD;(hks0f1^DIzGOtc5p5%ZN3
z{fx<OJP|QnN;suVMmDOw1Go>q{xvb!R5*w6<f~3Gml&~XTh2~Kv$8n>-FSSACg>nk
zPb+RnQysLlLen|XGF><-A#a9;5AJFYh~2UqKfm5{?y?1Q+ehw!V+|75O4pU{m$_jK
z$&yY?zV2+YtOPcy^7-Q?5I7A2{?s&%q=a-a%$zR&$O9d=Kns`3%XwJ~iTK9tzwAbA
zzjRW^)1_7%sF_Jrcy8@s7Oa<*-CM(ZVnEcG$Dr^CX4=9<h#J5(`%~0$W5x|9$kk-;
zRem6Yq(kdI-g%me&P^*f&t-5$u4R6qv=|j7gk~E=D#Te9M1ruPN+-};NJ-(E6Ritc
zq+DrTq{l%@?$rjeDC5sqLwsR-F1f~<FX&UEL<tMN^1ETBBqwiMD}|JD#*c-^qG&@s
zRjd24wu7bV2y!3nKM#o}Es8_>x;$}$|9m@R*h>}tni`T3!=rvg#P@ZM!DcF8dWAst
z+I&$qgQqzJe{|<_e&h8eU2O-f*xWx40qngPG;7sTTs@WX3kDHy{mSKmP2=*+8>yiv
zC0<5bFBRBQxqZ|Z6DSDKEnEsfIe}Y5*H=F+#3eo~Ns-|~ps_Krr|_hIrcEnZv-AC2
z$xki~?0NIN^PVR1ug=Ago?CaBHIFkD>6T92@eRXM*nCTsc0R!?lcQs(CM=+=g4TOA
zw1g$HN5p-G!b`gQ;x#b>m&!;Ua3b3cXbqR&{locujKBWm_>LRgT-jdwi8)z0WGC77
z9luTmD22_=-KF831@IB&vSzdQyoF9su8yH?vk!3cSe{|kG}a%eAwzOBt{9cctduf=
z=q$n)@2v*HRz`eAH@n~!HNsq2ZAFWNu7K1j(`Ya{T)9UYd&j&)Irn79IpN8mur6Wk
z-wf(8Dv*|W8p%6!kpcpp{l;^mOQ+ldoZH|v^t;Z@OgDG00pn;PFWbesc?xC+HQ>+X
z2h_LE(nY#ohr5^=yqxe9TnyB7_(heW78ZXaHQCV{k9m`-Lsz{|NSmR%LV^9Jh)ra*
z2rAmkyf<%X%l&@ZWr`R8{ni+xQ_849LY^l<r(>T>v@Hl|Dya!IzJOz7kqv?E_s@}O
z60crf6GRc$<=Tm>s7qbMgv}OAwp{s<u;G)T!kn06^>{vOB6lN-(vfu<AjJ9R<*}d~
zW$d2}z`yT}yjluZ4VK{4&7}8f*$JcloT*ZO%HYai9SPC~M>XF9Wc}zYY-{AC#zKn<
zVgjO-1NB_ol%^#<6L?$=#BvB2V^E8Xm+ukJtR4+f&zO$0nf2N0!yLtNb|)<>_3NT`
z4zLtz6bxm?cp#~Y_^n`$E0WyN)cjoCgygrtL3&YlQWNrJUX_;Sp3`aruJd_5CY@F2
z$)cwDyC<8FB<ZA-k^cS8z|=R%;J=j&2Lbi3RoljRiHkF!$ICdn$M>IGY^((uYgbwy
zHy)-7BaqXwgbVZD(n7w9`|>~ST{2Ah?_?ozt(&)lM8v&*!}XWUYQ=4#!Yl}wnYV)=
zz7ISpU-Q=6jKH9tu+}eflR_=_^P*6P<`w-SByVCmZuc)=l>^jCChYHUOk`_IE`2Xi
z+ZgoH+^n!Oo*@t!ub`+%UbBz!+Iw4t(#i12C`jed8&Am^a+rbzLmnjYG!2Lj-8gSu
zJ5~2<ooYb9(7f^@<}*)d5YsR)%H9{HWO*ciW2-<^!E>89&~4dU)qe`DB|IUli?K{?
zWmN{!jyq@-=~#_gdVRhgC(=+MTE{ND4cY4+Q5tlAH?>N8-+$YI1pMWy)B+Ba7}@X8
z<FaAmi1|?q2i+UMHQtc<RsqAsGQx=metR258p;e*lh5>FYth3QB&*!*wBwr7-I!{D
z{#hA5xWO{Er;6T$cLK%a2N#5iY_rFn_n>Zxq-Q0sOsQfJ;RCh$ncArt_gBICG8A8O
z`6}0l956|;W^G4@N(z}Ad9Kn3B*MgKV_IyaX~<%hnPDR&xjzi*^zxaW_#m^wY>=uj
zpm21WW)NBBHr?`Y?8l*PG%IOXGzI!p1+phu;Z9QnvH;yOXFXB(=L5{UqPX!wLmOG&
z_Xa~J97=we@K%MEG%mWRM1uw$5vl7-kDs-jCJT8oMpuq<R7RwBJB9QWnMSc5$X<&$
zEi2J*Y;B}*A@qNEv((G+<*dReaB^B@bW&|W$Za}a3onW_5royRQlUO~rZn`-Bv{g%
zqFa0!FP^F(x#GlId>q!1)nozI$4|*#)1+NbTbb8cWbv;!!jWh=Xq@Yy8?MZDmp&g&
z>e}AzTVx}zsQ1s@)bn)%BP;NHv*e(jchiFzrVfCmE9k4jbqA@0?e72JgyvXfDFeJF
zI}CsmOtyUi{VzBO2G-XOy8dD;*l(k+7}l{*Y=edu<^J)Sr%nQ&zs;-FEEx%ny9Bh0
z1?}hdbbGkhnudo>qlV0KgJ<n}I`2%CZg0h;jz6SS^P$2hbS+N0d1zBXo7BOuiSyM>
zG_i?*+OM2g??e35*|R-%f@FcCKQUfy?21*df~3U7hg`!klx~J6=-;mOo~Ta-Khj+|
z=3A_v8d%dj7~(`<)=+nmU5GVr$R{<Z>}rZflB{6=MBlfot7ss--Qsu<*|?9SqQQ43
z;~WC(fw*Ys8weuyQZiZFqC+4Z9pPsTn~dQeAz6eaZ{7S`RWzHNVJ8Ye?P6mv04}z7
zh)SB_6lN9kd90;R2+RDgE@Cpwv!y?dO<j-#T4Hhm-C1v%nC5+kBj2{ju0MX{B>Aih
zvCd75^&Uf>|F(7oGjHxW;@rksJLk~Wh;`=802CPVLuK2Bm6Dp2VLADn2O1U;@-E9(
zX=|ANl1@}i`SHS3M$V=cWlK8CAW@16RXEyHZf(U83S}((Q;vqeSECa_*I3D_Kj>4O
zGtoA0gxG_OkY{MUg9r?SLi~4f>GJdn=@}ecMo(?AfI0(yQbGVy^6dtrn<X4G{E<=Z
z7f!!ojK+J+PE=yl6yi5B*@j0eBVyfY0w8Dq7!4;ZY_LNaxm#&T(+~{o@Uv>mvhA2f
zR#hf#7M4U4G&*%PTQA8B{x#_yhq{M9bc?ENlE6ur3QUtrvmZx~lu}(yhj?kNT<A=H
z%h;1!50@JDho4nbae#g^1W7_rWl0{Yof`iX{{1E*hZ#|wDu1v_uaqA_G8G<%0_ivE
z!^H`qH(f8*Vk>2bZ1`}W78rMyxEGbQUY5aamgRZ3qdynV?g3K{?+s4FP&15GlwjAF
z1X1kxNZ6u-#^gEhx7K_3ye$IV{=S_tJ$kf3lV$t86g(Ihr-ivEP6Ra84|2K|+jseN
z*CxFHDI7XZaxbLjHHkC6NAYjHpb>6q3%|$$F<Rs=8j$r--LJws%}ol2Cm+2oq_-?q
z*jp~ySpOrY#H^BnaIy({DR+Ye`ls`NcJ{s$4a%`TBfS8Ony3Udr}I2xZD4j>b`*H%
zNwR~i>%m5?)jg#b#|&mxEz7o3?0N`$LaBOLE&2J#KHcdqJS;MbFu?8|ZrIK4n+lat
zT6f`6SlkyXk2YAxPWNQgsK2yx_EWQOR;Mp1JxdLo_j0XYBrb>a{9UgjkK=1zpTs-L
z@ta%qW#D;ZgZ5WnoJ28RUTh_lVgOk>qQrw6spRyC>@Qq)nA24V%473tfuCJPcTkB7
zbXw)S_Xz9wxxnY8lM$Tts%-*R$$oGy_vYdk_;^YYLs<Pgiqw_aPy?6<KN&FNP<E;*
z<lGj8Oz$?Z%`)^~i(R{O(1EWYH9)3LeiC$MQ$7Q8Uoo>V;|pKLav#{s<oVg)l>}E%
zS=Ch4-Td9?fv|#qrW5)Mo+M5WFdY4B<-JeE@-ektMjphBQRLfGWmdr0H9Kg?(bhev
z3X&Ka<>w72X;e0lbkqnFO~sRNky%$$7Xw42g~>uflLcd2oJN;AT+E3UPvUD<UleD(
zt?DfH$-~oDr}T<vznEN`kd|Rk<F*C}MTf&p01YjJa{yk4G3fRk*rQ;erf&ZBnk*1E
z#Xji0N?!jIKonM$dYgzD+A`)_0lcD1x&WelZo&&upC5imS%7}0Z9ZrEpUZR?hTh*@
z7t8q(Qeku@X^X|P?z4V5BWPy{W@36SGbi!g8|d(g{%iRwN|?_rn@dE}c^tz@U3$BL
zz39c**6P@yW0Yg88q@i=ROQ(I+TMLpUR_-rId<s3K%o)wZ~!Dj{Q6U6<=c+?uDTxv
zmK;~*?ad>9gV^oQmrFHgMk;ca%*I_vp{G%*r=WG)EJnTRhuZs;7tgE+L);OHOJf^M
z8!CRkx%<~6SZ=f94z?#bL+rV{t)~%*C8_4})rbhRW^CzH-W|i_W#Dj$Ie%hU6tDw{
ziF%(lhYlMC{o+-eh)Z;w#z-?P7CEy*x3on~i6oV6ugO$j=2l=4d#^;ckEXsT$oHFU
ztFxH{uYC;3SqEnyg4y%#C^NdL_KB&!do#C<zYEFe1<H7NZ9kwIvh?@s_`4%X$;YFM
z4~+RSnrAvv;!viyI2gYHIg7!c{k>$mDB<5K2=`YwE60;Nu%Z`QYRUg+)a^F9t-)~u
zICvg4MXR>d0-phjG&2s?=2t$Fvk@~e69T<Gwk&-BauaL1d~LgP=Z1IwW<2j(WF+bs
z+;5dLTNCSLxsc^k9P7tm_i1fH)%ClrKuHaG1H7UmW5B@Aiy%!gmetJG1Ue^MCmufg
zo}j%N(b%9{-2q{peVg6J(R4sRRo1F4mnKjtbjR8<<Xrmb2i5H{RIYQvIO<O1MHjH<
zJyn^M*YY1|*R2u)y~5Qxy0>!=zm${%==RG`d9_|z7|-=giM&@cgt=IU1kuAw*8JM5
z<}ahAFA_=j>jmW$e~z8Gt_{V-eM0l6YZSI+CLmkT%0-eb2NIVjocYrm<uE|y>E46U
zm}Dr<0^}R<L2=QG(-zkKs_5(=(ZB$9`!1&2b~j|QZp2OV0|;w<KkY7-*~+ALl%}LJ
z<^r}b@Qctm?0aZB>_hz<`-hpHDZnMVh?WY(bZj@u!vtxF2My@W7NFB;h<r(P5?PS^
zieOrK5n|gg58_U(XzDkZ?3LT+O`A7(reCr1>q!_X$j@W+K-aAZLNX(}v{Qm8?1jfy
z!dh?U99oZQmyMRi%?2vZJO@+gcq;;Il1WSRrIF~VaPcVNdClH7sFm?^!VA+CRo41-
z7QiaO)n1mslKDDP6S=+5F{`7EIbmdhujMlwg-=En>a)EB$l@Hnoz#KyeWMWrG<uM9
zW6d%!l%8#ivEnn|8XUMTV->VeHJE5H6r)tZ$nV?8uEySLZUPKph%r+a#231U2De2D
z^kU?6t1FAQ@e2060jzb|GrnufN?#y=4==>J8|A?5LMq7n%$(?cUuA41`LBk}U<E)5
z(BcojZOyLU;)D|0uGVWtKg=>8cNYz_1?dbjh9#lwUJR3YLXk~xf~FhX6z^GQG>Fa4
zt87&6{Ksm-WK}p~s`|jLbZcK?eBFG?)osJns^k?7%@2dp8n<8*>Nl$~3eG#S%to-P
zvg+Ai_OH)kIje>`ZzU#6T_WEjpU~Iml&Q-lYcBaLs0A?1=e{-FPpB{x)S)b_NCsj(
zhbjGyX!d?VBF*%wvhv&C*>MV@tRY~7Q9JIqzIO7>v?m%=;U84vKG}g5q~#gP&`jlo
zIJf$fhIt{PxmrdZu^zve-Ex5PW3VM24Ii@CJBb`pKChuJ(96X7GGvP@+|hnz=ZZu)
zYk$0}tv}jc4boBKU^GvO#gqDw1#948ueab3z)|0B88-W`C{ue{0}jrnoLbZsBYDV8
zD3vstE&q^taSvF#T7%4)+RVJDA|A~@^Rm3XCa7M=o;J5EmK&@PD1$ao8qK&JN|Qu8
z6ju962_=VNqE;e3cN!04#I1&}&5h3R{wi{EaOf<%@HBI#qeUQYYRyJ1jjq(f69O4J
zV_IdTL7TGl>`XP$d2ggt#uB25kZo&y!YQNx2=&ul!*#Ota)X>u6q#y_;EMRE(Uoju
z3*|3|SL$?oA(4gPRNrd*U46!Tf?{Ns7c{1!40x&l%(pnXZU?i2Kp2;@22wPcF?cod
z%Jp%%8y$b^sjsCWuIg!Jh&eIAry&pTE3-Q{po0JW$_|TSnnFe$-(5Srq2ZK?h}wn2
z#AZm~cmFWTnyrT4S!FQ2-y36(YzGo1Q62P4?*surxErgSDqqfix@`kU<KTg;S(oi7
zk5>6VVt$zagt)svuaa~77kpNyKY44tmx+eMtJpg5hI%bLGDHJhgB38y7<KSCt4PWe
z<a?U#2Z6Orf77>L_d31i`2CtfMkK&Vvl``JREmHXTP}A$ZNPUa*W^+s+uBEMLCDjq
zj0W4vbf!-SGWDL^5xcVb@s!UzNUwU(oX{1GUa(FoM11jv{0S$N;SqQBZ`JWbA|6L^
zc23K{Res*UOCFgmu<>~s>TFp`2+|MxBtrzjlA#}YLH7O&Qar1xUXsiTeYGaJ`&vXL
zdaEhppt|;sYLj8RRF|Go3T=3~zMs5CG{Y64jxYaMK)73v|HZ%WN7J2d$L&~(;7OJf
z$$5z!mjCV0?M9IhnCTcZJ(xSvSIp4Yxi0he%^oevs%|fP0urjLq~=+o%`Mm8{EQ<o
zkPX6fTJ(J`LOtHyn^z5-BEZPcPF1_*BZQ3?nqfwKqdnqxm;NegZQ-}XexF=KQ{ZW!
zOj{x^hrjyuK?&<}sL}!p9r2M|<=i^CU3~!yP=}U++>5Wwux1#jw2<HpwAMxfH!?)O
z1iDcIL^N3${X}c@dlz)~);$}4#K5S2H3l2=fUh%;?Qp^o6YFbKEU-rh+Ls|XmiAPN
zV<o2<CytVSD%YE;OQc_@A>AM(u<T)`EGkG$o-64c*P_k?B@MFh3w0VK!%Ud4&o4(J
z92lA$2S?NM=3e&p+~~35++?=z=Gt^CwEh6kZW;msHv!RP3OG5`M^6i5=*1;22|S#i
zBni!eeLGezGi8G22Gn`2_~QOGywV{G5#l^4{ZeYMv37+>3EJis_VC1{OirbPMcz+A
zYCbsalh>3VkJ610{e{;N`+j1NH{_uP0c+BEH}*JVAw;rPOh&csq43-mD)dpoUJiyc
zk5volBoc9;@ug3}GxzjFEAkMq>UXM(=#|fDhk!h!5lWnMp#*gp=P#R_ecWRD&@WYY
zvl8S=6lwE@Ch2!1Lb^_do|r#kKCIhq*eQSE1o4^`KU_q-^tI8tpi{%caeMgv!SL@e
zp4)|o;vv>KM`pK#Dt(HCj1ZIMyT(_QF}ltik!-wWz*XvLC%K~G+^7E*UxYdjG#;K^
zq*KvYEC$rNb1PkQ8^cw-sPri%G^lwtd7O$OS|1b{{@h3Nh3{>F6`xFA<7T5O7@AK!
zg!o~BYa~2_%}qG^ms;!0VZD^(*PG44*Ir)y4kh(<{{+I9R_k=&jO!UIFQZEv(*A(L
zClN1A6l`AKaPBr6b5}2u$8ux|*cvSfafBikd?szx;f2$R)LEXR=q!?+pQX5J@<bk)
zU3K&GH7Fpn^6XWYs`qeK`6IFatV*L3YsJY28){jM4)3k^SC=lyXYG!(p{35`*np(w
z!^Ft6ED;!2j(7k@A}U{~#>C(e9Vpc-4>DxlFA8p?j1`${nttBi-;O|j!!2}t#gSS*
znk`LhJm(v%>a?jx4mg&mVNxfg)pQ```Z-*B(Z4UnS7n6)`_K8<&OC_AF!@`i!%rp8
zy`l(>ZM#TNnxyoJ&J<+<vz50vQs@sub*g2?M8SbeCSK?GG7Xc)e91PC9&-S-rKCc%
z-JpKH>EaqW8V_97DA`Wv8bYq$S*RdG_~p<=YKPq)C4`R5ju~S^qT+->jC90>M;0he
zt^q=PQJ{|@OkaLJ=Ma{Pz<&wDQ$f;2i(C5T`$-UAMT5@!RBpZz&vZZ=Ro#)grt%rw
z?y*$T4ipUUyCMk_wq4yq9mER|f0xLqqg<+0;b6ax&i;I=T<Z%-Ghy>#Q%mAdQ+P|a
zK=u|!*?}k3T3F|LcGlxAFHvUclQpNGY==lovr}8C5JdRw-1LNgu#+-4{KOu+9JYFn
zkWndX8Xdi3i1NvWlkJPQm7l_K`e>L&PWT?&T*Sv(if4NX=`HK@_@vA)_VPB9B&vyv
z*ZRX>F-xq*Q;N;Ax82z}Qd+W2(Yo5DEo0O>d1k_E$4>U1j9&0(apNyz7U!JqeAqO{
z1}V_=JSn?!GocEt^g+C)&OVOAoov>kwB^cHZ`l*|`r=s719@s3Pfpdjg6)bxqTw9b
z{yn0>R<)ooUGPcu%HELOzjK$6=<s=z%d=F2h7m+|M9y)xaP8}{1-C&_Yg~NJk0J!o
zr@azQ6U;%wNaD!iZ{1$;gVW(AyU1)y{LZACkV8GtVR0;IXh}eqSXE$6UUkPrj&Nd$
zAIRn;MS?iXrGhdaO?t9Zd3`jC$DbxtugFQ%D(j0F%fan8ozfg*hne6C$SQiNc*HB5
z>4g?nga2~J9V{QnBVQyd45yH}rLvTxy{jqGl@W7GW$Xs#AdsKO*!wGw_S)o?ATdv*
z`tVd~q!O!n@)x(@IE<G=^qrs3)0*H)angyZpH({>ie#svaca-|y|tSX%G-0lfQ4OC
z&GJhZuGy_&iQqWWu**&5hJxp^90ms3PfnLFz3&EZvj%)}h)QT4p{|fJ96YM?%gN#N
zn+%RK6r!Syc+g%%J5B6l(;LsTfL9F@fe`kPb7r1vNcdb*0D^kpj|iIXwKmbj1hRWJ
znTX0+tY9_R6~qw&UDQeB2UO1|EKJlU`tr&$^irnqc;i}v5WU;zto@ga+iRPyd8kQN
zzlwD2J4&>&>tkEfuS&LFH`9eRIhn(5<W*#1I*J7#`~WTA+9go{S7csV6SiJ+ZIq>8
z*y7<jad&kOBcc=L+A=87guzT^_Gil9A&F>~TX4^_WQ{+!Rx_Uwtu%%E?$G+og%Qqp
zc=Bl&cxQVQa1nYqLxnsyX%&y-2<&gfCcX#9qp`ZgqZ9n$$ovI6DMurArO%(zpSaAH
z9pziEmxA_k&1E-UYl(_H^Ct|>wS*cQ6Opqa&5E)9lXZ)AM6$tWK8cZ(0#Q&MwMOEb
zG3omo12z^!JkE8;(;z$Tz1^y-T8AJC_$j7mBgNA-d=kL62qwv5Js2ANm(Pd;q20^{
za9#G$lnZLCqI(TBW(00D-&u42j*NE?>vZp((u5v+(Ap1S(yPv5LBMNcKaYIEz<qR<
z5P>}UqpCHxsI@8FF<y~P=}U5<=f<iw<ePL{M{C(1#0Jt86~I+TR+@z#m}vE4kDf_8
zit5zdc>MLbo^z|b=ySFexJX^(e}#&>_>+|owU2Oa=s0P=zU>~#GA8P`v(=!%e;>hZ
zol2o{PeIYFkf@tljeM=G+%;2tL5zYoIc{hS*sL~_tiBQZ_uJoH%o}YT%;Cx^BRe1m
z6$s+P=eyo{fulO|MW#{GJK^+;S3vbjC)OYy{~(*bZEK_yz!6a)XlexSpO+>0^y69~
zc2~+_EEDbg)u`2JOt1-4qEhhs5h5ICTV}~X)YjJ$T5=hXN@_nrZ-4TaMW<r-v0=y=
zG=$zts{}2eC?b@z=2_uov;|7mW5Z6`NNEnJfhziVWl#N7=vBrZ)WYN5GU@WG#Gg9^
z7bb4#(nmSetksw0*_Kt77NUf)hhui6atG-qI(l5oxWCQsyN!YYD_B#{>*G;q|7i|z
zP>2iZm~#bS)_`oNA(DE<-~Z$A(l-nFm87EEjnaa$H-T?U2X;;z*rQn(<~_PsDW%Dz
z+4UPhD3?b*a=8WSL-5pwY<7)0zgdRbU!NG%@Ygy5udV<@lE;PMO2*iG$l@|p@8jL}
zH?rZO8+ZH29GO^Eiy00|61CiTQB`jNzDAgNu85&Gp;xJNtLO6?a=eBk6XClIHE#@+
z57@TSt*3qAtgOrvs6Pf%Z-rp}{JzmDcOuBxfh_Ddn%I?vwNMa@?C&=U?$+hzIXC?#
z-|8$YT{^9H4lJOb)eWcnoMpk-h;ByIVKGf`n%gv^i9!p9^5JC>;NkNzEwi!<nPiB8
zwv|4L`#AITqYRDf0$P}L5M8rGQE0s1KObRgB7uU6GaV#f*=l9rhrCBCB!c`^19s&A
z&g|~MVTm!asBo(Md(`_@Mc(gjc!jEaNS}LbVk#qQbuSvuY<Kx6c>U4ngTvw^-+Pp;
zIJlzfUV$$@Hv=#+CBY%gPJ$3%_~e!x(Da}CYj=?Q^5wrwO?jaJa{+|tKE6q%&x+Ml
zcpe-A!(7yiH)=So_y;-_n)8xB6v-ZbP|~Ky`p-6IhQmI`e4C+K?-*A>js+(@h|rY+
zvd_M&y7h**1~fbKejj#FJ^@(j^Cb;y1yU$A2BYBpN7I|dCSJHxo`m&4M?p>G1@8`&
z?yzjcyM*$`L9f2w!Uq1H+HlA+oP(IT`Z~%abEbV8(_}2EPF<PPBuV!RbcgsFjW=7|
zb_Frgpl`#UgA;JVYFdzUja>|7McXr(w1C<@C!Kofw>z2=wnv^k6Lf&L{rpMV-&+>0
z<<GLNy6DNObLxY!8JLU5hcuzZ(bO?!N~?+EF7m}34+eHq4wf>rUABdNJR=CGSo~;?
zJpO>E^!71+{bzkM_<_4e+)~@FW8X8ZQM>5mtFr15G@KK1&Do;dycuhIH?WqIFJOF|
zKhwdQ!V|jz_rO5@QM#Jre=VX%L&+(yb<~9uW>(qT^iw8gFI#Gln{xvKQ8l;t>+`W#
zpgCi!lSoAcT9SCy?NZF0B=7TZx5NVh3e)#LF!MZ{bJq_bL~JSM`6=2&qSs+cQ;TZB
zFK{jI!-8#nZioNQdiCx_=0oN*BD3eKo9>gPx1gPT9gvUpS+9(6lelDX#KmpV1VLlU
zPgT{dsyi=1zJ}!U6-Z&&XA*JmBamY$zq8I-%{9PcDwMFzX9-WZBe<fAIzJ~PnY~DU
zW?Y`|T<R}2Vc)S`e4_RUle}F1zV%&SN`T%7|5ZVu1LwMWL)T1w!lSVQ-7gDhR63QT
zSyKq<G0p`Bm#e@5coVPCQduTHz0zEeGgBQ*kub^dtS-|zeNTQR30Z^BtT}{?u~sHA
zy3#HmjY{TYPnj54k#Y{|_Ox#HIL9VDEoB9+!j^oP*=IBmEZkWnZSheP62BMF{`R0E
z(6lF>?km(Lg<~P&)4z{m%C9<wsNIVAZP4&<15RHDKV;mEf_~#5*UI-k*t}vxrV8_~
zo@^c1;bs?QSrJ$hJLv)OaMNQcNa9D57bmU=var+Doo$IPOQWt;@t07O<3e`x_)W3&
z@0JR;+8Hz37=XpKgvlb^Ph9<O&ODFK5Es?XDcjdfdt~F&mw<BO$5H6D*@69?5Qz{1
zZ;Zfz+Q1R=co1rh_PcdLZ8WtS!v0J(ruGT8x~$s6iMIwAgs3u`1ZTU6os-PyDH^?M
zDL0GrPLGzx8^L|br<PMV#=VPCURz2_Wn^=JA@pSkW466{fb}F(5c_UK8fyCUn-5%`
z&mN7s+B+I;@cR1s#!ZsHS&}3=%RXh(jumc+$)%+0L_6KyUB#w!NsRt<_x@V2F!L{*
zE$OtjxDKM#mXh~ZA!%bOK?vzuPt1MP>&OhB-{1}5pB(r&4U3UwyePhUE;NelN2PDN
zo?0QkX{6BvFN)qW0lgH7zHB;Q;${#eqo@<2j)nGE@O2os4>7b^f8VlkOavfwpmedD
z0VSkE=fsOL2rf&1eC-I8@3G&66&?CVgd8I%^zO_LMZBTt3<W9PCYy$rfm+I%6kG3l
z5C@IL(Y!K$cgZ=*b5opr70_AoR2Hb2F7{f%`0_PJZLZ<rA&-i{xAW7T@E=0~mx`I>
zuTBd9NHx-SnNxVA*dkV^Zh}OfE>I*({iM|^RBP?6CW+*T7vM5ye#Yr=b2=(PV^6>h
zK?P!7a2l&$iw}5=#AwwSGS8FgxQ?MKV_ElPJA+z-qlb91A7FsBrg}wzaRt=$em$Rt
zm?(L?pci9}vR+b;9@a&4Ijz@{@Y?ZRLT`S(yv4g8o!J+(WtoQ7l{R`Rj`fflJv9vP
zQP6tcEYMJAfX{^+gmj_hKqo+vLy-W+uS82%)cq~Yhal&nJJ2aEkt7qiv2PI-e)fGY
zx-+<E;xuN6S30dNx7G%!i|&76HO3gHbHCd|_5v?7-oJi*Sn(I>Od|5P;G0}PZ1SEC
zJj}+QBc+8mUr8CkfnysM=)&v+KCpO|spc2K$Vn_+V#x;f^iN$K5)x4BLcLS3lI_O6
z;R-}i&Q4={e|f}gv-!lfOBip9WMVYz4X@tf&c6(&>ihO<B{MAt^PEM4Q*4l<vLdha
zssqi#u0)AvjFNG+=#!(>w+CnnRkDTsL1&x=HK>PCn1M)a{mVYcdv>lub4v+cPHVZ0
z9^@5_^4YsbiK-N9!)FuJh$<b7=h_Wa{6MD8F}pu>K$<aHe+o`#9^Br&P*IsF<??qx
zzJX0<p0~D0bF4<9d4CTw7vVy~HvCOo64xnoRIKJpfh~taq3w9w95USt0^FHhu5klk
zGi8igEgKa~pP=%!vIko&d|WNkjtbAL1UHFM)rTGDEQ03BcUrMf$sv%Qj$ecY2`^9*
zXv1>?(`R|73x1}VF)nh+VKXB8QHmf(93VpiriV^A<%-y^_CsxHU)9!C%+NXh>vP@{
zw#8L#RBE{^4^fbDv;>4PPhQ?H?MG+1_E#}q9g)6f&>Z}!olO@WI^mOUTS%kKE+%;F
z4<(iyQmrf2WqV~u274#&f%dw$*+Q^=aXtCy`_tzMUx>8AfjVeq9(Evv<;IPY)<PGu
zG}^KS$43%Ez<AM^HL`e;z-D-TCno_y0<c1!Jv$J-QIA7&IPApk$DQb&28WmSXAskl
zm<qMFS$>B=*n?#m)U4U@X~iiqjIYHNuJ7y4k?^GKo!a)?&jU3u_%>EY)9SA_%hYoZ
zpZpb(5x$o`EEleg)$P|?*J6~GTH!%HH?>cIane3DT}BqI#CCgYJ=>R1V>&5=#G%(C
zyQwi4GI-E)7S^7li@zYgcHYIczIYc-gGI_KUSgcaaUky|l<j%kLt6e^iTp=Iii529
zp>!sYc3~ga!zU`w@A`*S?n2nlXG^Q*+g9D5dot?4u@zZyb7Wp3aq}`S{3oKp7eEH0
z7jG*#z495cU@{y>2a@mXdi3I}_(e^$Zq$t!l5p%6vy;rCY%*A&;;z*EDh|*E>-VCB
zXS^y*G6&c@?t};;U1OSmd!y4;Gx895$CvLzocWYoURWSIhunq1Q$>DIA0|v0A6Y#Y
zkkF?}J)ut$rPd#^7zjGcEW^j?l582iGe}@5E9rB6DT+oIRSh)@zs^oUxoOfWV@3~F
z-3GC|p3H7Jh@=R7F|G5|oR!-6z%GU{i&t2E=3MXg3R_dVKzEU*B4Holv@qD_$bL2o
zR?jUD1<Yq7Q)eRpXJm}R{KuXnyEtTrtPSj?mOX|OL1G3ZIkNwf@#8-MS8H~_E|XZ1
zp*=&T@sI4{#CPZZbP?Km5n@$}CiK)1z_1vvr0_xSH>6R4jwFeju<7jJVfS6o4|Dg-
zMzRC!x;I|4LIw09FCNFskQq;LS|!c1`keISMvH5kjwj3H2x#`Fy?Lykl9KcQr|`ws
z)A2EU9|8tk0pPR$=Us1I*Uuvl_tF}b5c|U6<POck<oo1=mOayPK;4XBF^}4_qEo+$
zYJCqCD3~dKfHw?C6;r4Y#&?gtLY8dGjU3l|wR^~y%b^e*A$DTw=`pU2VHkO4?ib_n
zc2ctec?1R{qARp|JT0XNNz~apr+vENoD*6)Q8XNsaonU%E{hst(iWlwTaPrS0*s(6
zT{EIunk?s|kBOK+Ig04+kNbs#fa(6pT|Md63blGdTg|Eqy+z=4#~so*&0p1$#ltAj
zs(Hv@i}2@PzhozCXZ{qpD^`03ZWpF?_J5=MJ}%clCukgrim>v6*49ca3Tw0gx5Fl|
z<{&nFi+X->u7`+z!;CC$i3`)tT*sUj!B#;E?&<jgTPp2X9p&zgN1$!r_liVtV6w^v
zDe9$WKMUu&$J96->Ja~!5z=ftvb|G}%(?A6E-v}24akD$j4nPMpCj_VQ@Wg<h;^fU
zpY-R4o8KKV7#;SE*;!%#=pz}@x(7a8%o<iIPZr@A97D(s%eASPD@nWy`l!v;5d5YY
zre6${ZQ5Y^NZ<HXI8WV-Opz=gg>)YyW(YxKPKQe~!HBllIYvwMBA0c#^Onbv?uS+#
zq3u~)*W<5a8ycg8s+|;ALDVtCYnVB=f#9OmsXGWrX}%pwQpG3tNxU;L&SJFIKHltd
z5L{_VQ2eJ<hWw9GSpg=1g&nyUib&{j`fSBquNON5!;EP!!!OA9vHtDbfG;3%Stmgz
zWXD>g7Wd#+r_Hlqvv&uYH$uQaZ8XIH6y9=jKs5h6C?Mc)OCYd+=${@M2<PARPqUr>
aUnThe6w~=2{}%;$$OrvD{9lnb(EkI>Cb-Q2

literal 0
HcmV?d00001

diff --git a/public/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/large-result-0.avif b/public/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/extracted-outputs/large-result-0.avif
similarity index 100%
rename from public/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/large-result-0.avif
rename to public/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/extracted-outputs/large-result-0.avif
diff --git a/public/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/large-result-1.avif b/public/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/extracted-outputs/large-result-1.avif
similarity index 100%
rename from public/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/large-result-1.avif
rename to public/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/extracted-outputs/large-result-1.avif
diff --git a/public/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/small-s4-code-0.avif b/public/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/extracted-outputs/small-s4-code-0.avif
similarity index 100%
rename from public/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/small-s4-code-0.avif
rename to public/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/extracted-outputs/small-s4-code-0.avif
diff --git a/public/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/small-s4-code-1.avif b/public/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/extracted-outputs/small-s4-code-1.avif
similarity index 100%
rename from public/docs/images/tutorials/simulate-neutron-scattering-with-a-qiskit-function/extracted-outputs/small-s4-code-1.avif
rename to public/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/extracted-outputs/small-s4-code-1.avif
diff --git a/qiskit_bot.yaml b/qiskit_bot.yaml
index df8a4a9c3f3..097ef95f7ff 100644
--- a/qiskit_bot.yaml
+++ b/qiskit_bot.yaml
@@ -833,7 +833,7 @@ notifications:
   "docs/tutorials/simulate-neutron-scattering":
     - "`@nathanearnestnoble`"
     - "@kevinsung"
-  "docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function":
+  "docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow":
     - "@pdd23001"
   "docs/tutorials/readout-error-mitigation-sampler":
     - "`@nathanearnestnoble`"
diff --git a/scripts/config/notebook-testing.toml b/scripts/config/notebook-testing.toml
index 09cd5061827..f6e5b24e06d 100644
--- a/scripts/config/notebook-testing.toml
+++ b/scripts/config/notebook-testing.toml
@@ -226,7 +226,7 @@ notebooks = [
   "docs/tutorials/compilation-methods-for-hamiltonian-simulation-circuits.ipynb",
   "docs/tutorials/solve-market-split-problem-with-iskay-quantum-optimizer.ipynb",
   "docs/tutorials/simulate-neutron-scattering.ipynb",
-  "docs/tutorials/simulate-neutron-scattering-with-a-qiskit-function.ipynb",
+  "docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow.ipynb",
 
   # Don't test any learning notebooks
   "learning/courses/quantum-computing-in-practice/introduction.ipynb",

From 39b93335e05015b26faf01687fe2a50f278dea05 Mon Sep 17 00:00:00 2001
From: Parth Danve <parth.danve@uconn.edu>
Date: Wed, 12 Aug 2026 16:40:19 -0400
Subject: [PATCH 06/25] polish

---
 ...simulate-neutron-scattering-with-a-serverless-workflow.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow.ipynb b/docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow.ipynb
index 8f6591144ce..8f637cef2bf 100644
--- a/docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow.ipynb
+++ b/docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow.ipynb
@@ -894,7 +894,7 @@
     "| 20 | 24m 52s | 15m 58s |\n",
     "| 30 | 230m 57s (about 3h 51m) | 17m 39s |\n",
     "\n",
-    "Queue time is not counted in either column. The classical stage climbs steeply, roughly 6 times from 10 to 20 qubits and another 9 times to 30, dominated by the AQC fidelity optimization at `max_bond = 128`. QPU usage grows only about 1.2 times across the same range, because the circuit count and shot budget follow `t_steps` and the twirling settings rather than the qubit count. At 30 qubits the classical stage costs roughly 13 times the QPU time, which is the practical argument for rehearsing on `statevector` or `fake` before committing to hardware."
+    "Queue time is not counted in either column. The classical stage climbs steeply, roughly 6 times from 10 to 20 qubits and another 9 times to 30, dominated by the AQC fidelity optimization at `max_bond = 128`. QPU usage grows only about 1.2 times across the same range, because the circuit count and shot budget follow `t_steps` and the twirling settings rather than the qubit count."
    ]
   },
   {

From db3d4cda2eb4b0ed7a53fab55cb2aa2565c96de2 Mon Sep 17 00:00:00 2001
From: Parth Danve <parth.danve@uconn.edu>
Date: Thu, 13 Aug 2026 09:36:34 -0400
Subject: [PATCH 07/25] hardware output code

---
 docs/guides/function-template-aqc-trotter.ipynb | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/docs/guides/function-template-aqc-trotter.ipynb b/docs/guides/function-template-aqc-trotter.ipynb
index c1d7ba0d496..61060fca6d7 100644
--- a/docs/guides/function-template-aqc-trotter.ipynb
+++ b/docs/guides/function-template-aqc-trotter.ipynb
@@ -127,7 +127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 76,
    "id": "auth-code",
    "metadata": {},
    "outputs": [],
@@ -539,6 +539,8 @@
     "# Run this only once the status cell above reports DONE. result() blocks until\n",
     "# the job finishes, so calling it earlier just waits.\n",
     "result = job.result()\n",
+    "print(\"observable labels:\", result[\"observable_labels\"])\n",
+    "print(\"times:\", result[\"times\"])\n",
     "print(\n",
     "    \"AQC fidelities:\",\n",
     "    {k: round(v, 4) for k, v in result[\"metadata\"][\"aqc_fidelities\"].items()},\n",

From 2bc36766d9b04715c93cc2409edb20e8506f4e51 Mon Sep 17 00:00:00 2001
From: Henry Zou <henry.zou@tufts.edu>
Date: Thu, 13 Aug 2026 11:46:25 -0400
Subject: [PATCH 08/25] Add hours/qpuSeconds usage metadata to tutorial
 notebook

---
 ...mulate-neutron-scattering-with-a-serverless-workflow.ipynb | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow.ipynb b/docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow.ipynb
index 8f637cef2bf..3613a463001 100644
--- a/docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow.ipynb
+++ b/docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow.ipynb
@@ -932,7 +932,9 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3"
-  }
+  },
+  "hours": 4,
+  "qpuSeconds": 1080
  },
  "nbformat": 4,
  "nbformat_minor": 5

From bf69584556bf218a5b0700db8ba7a6eb87be80e3 Mon Sep 17 00:00:00 2001
From: Henry Zou <87874865+henryzou50@users.noreply.github.com>
Date: Thu, 13 Aug 2026 12:09:23 -0400
Subject: [PATCH 09/25] Update docs/guides/function-template-aqc-trotter.ipynb

Co-authored-by: abbycross <across@us.ibm.com>
---
 docs/guides/function-template-aqc-trotter.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/guides/function-template-aqc-trotter.ipynb b/docs/guides/function-template-aqc-trotter.ipynb
index 61060fca6d7..a4f7a53a4f6 100644
--- a/docs/guides/function-template-aqc-trotter.ipynb
+++ b/docs/guides/function-template-aqc-trotter.ipynb
@@ -41,7 +41,7 @@
     " + optional local kick      -> <O>(t)                               quench dynamics, ...\n",
     "```\n",
     "\n",
-    "The template is published in the Qiskit Function templates repository, alongside the other application templates. See: [AQC Dynamics Template](https://github.com/qiskit-community/qiskit-function-templates/tree/main/physics/aqc_trotter). This notebook deploys it to your own IBM Quantum&reg; Serverless account. Run it once, and any notebook can then call the function with `serverless.load(\"aqc-dynamics-function\")`.\n",
+    "The template is published in the [Qiskit Function templates repository](https://github.com/qiskit-community/qiskit-function-templates/tree/main/physics/aqc_trotter), alongside the other application templates. This notebook deploys it to your own Qiskit Serverless account. Run it once, and any notebook can then call the function with `serverless.load(\"aqc-dynamics-function\")`.\n",
     "\n",
     "For a worked scientific example, see [Simulate neutron scattering with an AQC + Trotter dynamics Serverless workflow](/docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow), which calls this function to compute the dynamical structure factor of KCuF$_3$. This notebook covers deployment and the input contract instead."
    ]

From 57cc22bb601df7bb836d8b4cd90c58ad6a5a7e9f Mon Sep 17 00:00:00 2001
From: Henry Zou <87874865+henryzou50@users.noreply.github.com>
Date: Thu, 13 Aug 2026 12:09:38 -0400
Subject: [PATCH 10/25] Update docs/guides/function-template-aqc-trotter.ipynb

Co-authored-by: abbycross <across@us.ibm.com>
---
 docs/guides/function-template-aqc-trotter.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/guides/function-template-aqc-trotter.ipynb b/docs/guides/function-template-aqc-trotter.ipynb
index a4f7a53a4f6..87b174b2b8c 100644
--- a/docs/guides/function-template-aqc-trotter.ipynb
+++ b/docs/guides/function-template-aqc-trotter.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "---\n",
     "title: Deploy and run a Qiskit Function template for AQC + Trotter Hamiltonian dynamics\n",
-    "description: Deploy the AQC + Trotter Hamiltonian dynamics function template to IBM Quantum Serverless, then run it on a simulator and on a QPU.\n",
+    "description: Deploy the AQC + Trotter Hamiltonian dynamics function template to Qiskit Serverless, then run it on a simulator and on a QPU.\n",
     "---\n",
     "\n",
     "{/* cspell:ignore Trotter Trotterization quimb cotengra cotengrust Suzuki fidelities isa */}\n",

From 1ded582dca2d45a9338884efdd86623e8c692ff1 Mon Sep 17 00:00:00 2001
From: Henry Zou <87874865+henryzou50@users.noreply.github.com>
Date: Thu, 13 Aug 2026 12:12:55 -0400
Subject: [PATCH 11/25] Update docs/guides/function-template-aqc-trotter.ipynb

Co-authored-by: abbycross <across@us.ibm.com>
---
 docs/guides/function-template-aqc-trotter.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/guides/function-template-aqc-trotter.ipynb b/docs/guides/function-template-aqc-trotter.ipynb
index 87b174b2b8c..3e66abfddad 100644
--- a/docs/guides/function-template-aqc-trotter.ipynb
+++ b/docs/guides/function-template-aqc-trotter.ipynb
@@ -109,7 +109,7 @@
    "source": [
     "## 1. Authentication\n",
     "\n",
-    "Use `qiskit-ibm-catalog` to authenticate to `QiskitServerless` with your API key (token) and CRN (instance), which you can find on the [IBM Quantum Platform](https://quantum.cloud.ibm.com) dashboard. This will allow you to locally instantiate the serverless client to upload or run the selected function:\n",
+    "Use `qiskit-ibm-catalog` to authenticate to `QiskitServerless` with your API key (token) and CRN (instance), which you can find on the [IBM Quantum&reg; Platform](https://quantum.cloud.ibm.com) dashboard. This will allow you to locally instantiate the serverless client to upload or run the selected function:\n",
     "\n",
     "```python\n",
     "from qiskit_ibm_catalog import QiskitServerless\n",

From 614b8359a34ed2b717af2788c7a3345d27af5890 Mon Sep 17 00:00:00 2001
From: Henry Zou <87874865+henryzou50@users.noreply.github.com>
Date: Thu, 13 Aug 2026 12:19:49 -0400
Subject: [PATCH 12/25] Update docs/guides/function-template-aqc-trotter.ipynb

Co-authored-by: abbycross <across@us.ibm.com>
---
 docs/guides/function-template-aqc-trotter.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/guides/function-template-aqc-trotter.ipynb b/docs/guides/function-template-aqc-trotter.ipynb
index 3e66abfddad..8d1b2d70a75 100644
--- a/docs/guides/function-template-aqc-trotter.ipynb
+++ b/docs/guides/function-template-aqc-trotter.ipynb
@@ -116,7 +116,7 @@
     "serverless = QiskitServerless(channel=\"ibm_quantum_platform\", token=\"MY_TOKEN\", instance=\"MY_CRN\")\n",
     "```\n",
     "\n",
-    "You can optionally use `save_account()` to save your credentials in your local environment (see the [Set up your IBM Cloud account](/docs/guides/cloud-setup#cloud-save) guide). Note that this writes your credentials to the same file as [`QiskitRuntimeService.save_account()`](/docs/api/qiskit-ibm-runtime/qiskit-runtime-service#save_account):\n",
+    "You can optionally use `save_account()` to save your credentials in your local environment (see the [Set up your IBM Cloud&reg; account](/docs/guides/cloud-setup#cloud-save) guide). Note that this writes your credentials to the same file as [`QiskitRuntimeService.save_account()`](/docs/api/qiskit-ibm-runtime/qiskit-runtime-service#save_account):\n",
     "\n",
     "```python\n",
     "QiskitServerless.save_account(channel=\"ibm_quantum_platform\", token=\"MY_TOKEN\", instance=\"MY_CRN\")\n",

From 1f0cecce96668d4be6f2603a8b5a08f1aa7253bb Mon Sep 17 00:00:00 2001
From: Henry Zou <87874865+henryzou50@users.noreply.github.com>
Date: Thu, 13 Aug 2026 12:20:14 -0400
Subject: [PATCH 13/25] Update docs/guides/function-template-aqc-trotter.ipynb

Co-authored-by: abbycross <across@us.ibm.com>
---
 docs/guides/function-template-aqc-trotter.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/guides/function-template-aqc-trotter.ipynb b/docs/guides/function-template-aqc-trotter.ipynb
index 8d1b2d70a75..7b57727cc50 100644
--- a/docs/guides/function-template-aqc-trotter.ipynb
+++ b/docs/guides/function-template-aqc-trotter.ipynb
@@ -255,7 +255,7 @@
     "| `estimator_options` | DD, twirling, TREX | `EstimatorV2.options`, passed through as-is. A supplied dictionary replaces the defaults wholesale rather than merging into them. |\n",
     "| `transpiler_options` | `{\"optimization_level\": 3}` | `generate_preset_pass_manager` keyword arguments. `backend` and `target` are rejected, since the execution path owns them. |\n",
     "| `backend` | `\"runtime\"` | `\"statevector\"`, `\"fake\"`, or `\"runtime\"`. |\n",
-    "| `backend_name` | least busy | IBM backend name for `runtime`, or a named fake backend. |\n",
+    "| `backend_name` | least busy | IBM&reg; backend name for `runtime`, or a named fake backend. |\n",
     "| `batches` | `1` | Split the circuits across N runtime jobs. One batch submits a single job and creates no session. |\n",
     "| `parallel_sim` | `False` | Fan the local simulator paths across all available cores with Ray. No effect on `runtime`. |"
    ]

From f28071bb093bf80da091c979ce49a243c470a640 Mon Sep 17 00:00:00 2001
From: Henry Zou <87874865+henryzou50@users.noreply.github.com>
Date: Thu, 13 Aug 2026 12:21:11 -0400
Subject: [PATCH 14/25] Update docs/guides/function-template-aqc-trotter.ipynb

Co-authored-by: abbycross <across@us.ibm.com>
---
 docs/guides/function-template-aqc-trotter.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/guides/function-template-aqc-trotter.ipynb b/docs/guides/function-template-aqc-trotter.ipynb
index 7b57727cc50..9571c53377a 100644
--- a/docs/guides/function-template-aqc-trotter.ipynb
+++ b/docs/guides/function-template-aqc-trotter.ipynb
@@ -328,9 +328,9 @@
    "source": [
     "## Simulator example\n",
     "\n",
-    "Run the function on the exact `statevector` backend first. It spends no QPU time and validates the deployment end to end. The model here is an 8-qubit transverse-field Ising chain, and `observables` is omitted so the function measures the default per-site $Z$.\n",
+    "Run the function on the exact `statevector` backend first. It spends no QPU time and validates the deployment end to end. The model here is an eight-qubit transverse-field Ising chain, and `observables` is omitted so the function measures the default per-site $Z$.\n",
     "\n",
-    "The compression plan is the input worth understanding. Each segment `{\"n_steps\": k, \"ansatz_steps\": m}` compresses `k` consecutive Trotter steps into an ansatz built from an `m`-step Trotter target, and any steps beyond `sum(n_steps)` run as plain Trotter. Early, low-entanglement steps compress well into a shallow 1-layer ansatz; later, more-entangled steps need a deeper one."
+    "The compression plan is the input worth understanding. Each segment `{\"n_steps\": k, \"ansatz_steps\": m}` compresses `k` consecutive Trotter steps into an ansatz built from an `m`-step Trotter target, and any steps beyond `sum(n_steps)` run as plain Trotter. Early, low-entanglement steps compress well into a shallow single-layer ansatz; later, more-entangled steps need a deeper one."
    ]
   },
   {

From 9d3d372824e4d979e67ef7765b2c8c7719fcf48d Mon Sep 17 00:00:00 2001
From: Henry Zou <87874865+henryzou50@users.noreply.github.com>
Date: Thu, 13 Aug 2026 12:21:23 -0400
Subject: [PATCH 15/25] Update docs/guides/function-template-aqc-trotter.ipynb

Co-authored-by: abbycross <across@us.ibm.com>
---
 docs/guides/function-template-aqc-trotter.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/guides/function-template-aqc-trotter.ipynb b/docs/guides/function-template-aqc-trotter.ipynb
index 9571c53377a..7be6918cf1c 100644
--- a/docs/guides/function-template-aqc-trotter.ipynb
+++ b/docs/guides/function-template-aqc-trotter.ipynb
@@ -393,7 +393,7 @@
     "|---|---|\n",
     "| `RUNNING: OPTIMIZING_FOR_HARDWARE` | state prep, Trotter build, AQC compression |\n",
     "| `RUNNING: WAITING_FOR_QPU` | queued on the QPU (`runtime` backend only) |\n",
-    "| `RUNNING: EXECUTING_QPU` | circuits executing (local sims mark this directly) |\n",
+    "| `RUNNING: EXECUTING_QPU` | circuits executing (local simulators mark this directly) |\n",
     "| `RUNNING: POST_PROCESSING` | assembling the result dictionary |\n",
     "\n",
     "Terminal states are `DONE`, `ERROR`, and `CANCELED`. This `statevector` run has no QPU queue, so it skips `RUNNING: WAITING_FOR_QPU`. Use `job.logs()` at any point to see the per-stage logs, including the AQC fidelity reached at each step."

From e297a83d99bd95055ba30845a7e681a208b2a87d Mon Sep 17 00:00:00 2001
From: Henry Zou <87874865+henryzou50@users.noreply.github.com>
Date: Thu, 13 Aug 2026 12:22:04 -0400
Subject: [PATCH 16/25] Update docs/guides/function-template-aqc-trotter.ipynb

Co-authored-by: abbycross <across@us.ibm.com>
---
 docs/guides/function-template-aqc-trotter.ipynb | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/docs/guides/function-template-aqc-trotter.ipynb b/docs/guides/function-template-aqc-trotter.ipynb
index 7be6918cf1c..6cef768c07c 100644
--- a/docs/guides/function-template-aqc-trotter.ipynb
+++ b/docs/guides/function-template-aqc-trotter.ipynb
@@ -494,13 +494,13 @@
     "\n",
     "A hardware run is not quick, and most of the time is classical rather than on the QPU. The AQC compression runs inside the function before anything reaches the QPU, and the QPU queue is on top of that. You do not need to keep this notebook or kernel open while it runs.\n",
     "\n",
-    "Copy the job id printed above and save it. The next three cells let you pick the run back up later:\n",
+    "Copy the job ID printed above and save it. The next three cells let you pick the run back up later:\n",
     "\n",
-    "1. Reconnect, only needed in a new kernel session: re-run the [Authentication](#1-authentication) cell to recreate `serverless`, then rebuild the `job` handle from the id you saved. Skip this cell if you are still in the session where you submitted, because the handle is already live.\n",
+    "1. Reconnect, only needed in a new kernel session: re-run the [Authentication](#1-authentication) cell to recreate `serverless`, then rebuild the `job` handle from the ID you saved. Skip this cell if you are still in the session where you submitted, because the handle is already live.\n",
     "2. Check status: re-run until it reports `DONE`.\n",
     "3. Fetch the result: run only once the status is `DONE`.\n",
     "\n",
-    "Paste your saved id over the placeholder in the reconnect cell below.\n",
+    "Paste your saved ID over the placeholder in the reconnect cell below.\n",
     "\n",
     "</Admonition>"
    ]
@@ -512,9 +512,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Reconnect to a previously submitted job by its id. Only needed in a NEW kernel\n",
+    "# Reconnect to a previously submitted job by its ID. Only needed in a NEW kernel\n",
     "# session; if you are still in the session where you submitted, the `job` handle\n",
-    "# above is already live, so skip this cell. Replace the id below with your own.\n",
+    "# above is already live, so skip this cell. Replace the ID below with your own.\n",
     "job = serverless.get_job_by_id(\"<your job id>\")"
    ]
   },

From ef040b4ec5b4247953996811e29e10da5cd8740c Mon Sep 17 00:00:00 2001
From: Henry Zou <87874865+henryzou50@users.noreply.github.com>
Date: Thu, 13 Aug 2026 12:24:30 -0400
Subject: [PATCH 17/25] Update docs/guides/function-template-aqc-trotter.ipynb

Co-authored-by: abbycross <across@us.ibm.com>
---
 docs/guides/function-template-aqc-trotter.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/guides/function-template-aqc-trotter.ipynb b/docs/guides/function-template-aqc-trotter.ipynb
index 6cef768c07c..f5f70cdcccb 100644
--- a/docs/guides/function-template-aqc-trotter.ipynb
+++ b/docs/guides/function-template-aqc-trotter.ipynb
@@ -151,7 +151,7 @@
     "\n",
     "Packages the function needs on top of the managed base serverless image.\n",
     "\n",
-    "> The gateway only installs names on its allowlist ([`requirements-dynamic-dependencies.txt`](https://github.com/Qiskit/qiskit-serverless/blob/main/docker-images/requirements-dynamic-dependencies.txt)), matched by package name and pinned to the allowed version with `==`. Anything else must arrive transitively (as a dependency of an allowlisted package). `[extras]` *are* honored — `qiskit-addon-aqc-tensor[quimb-jax]` is what drags `quimb` / `jax` in here. `cotengrust` is needed for memory efficiency during tensor network simulation. `qiskit-aer` is listed separately for the `fake` backend (local noisy simulation)"
+    "> The gateway only installs names on its allowlist ([`requirements-dynamic-dependencies.txt`](https://github.com/Qiskit/qiskit-serverless/blob/main/docker-images/requirements-dynamic-dependencies.txt)), matched by package name and pinned to the allowed version with `==`. Anything else must arrive transitively (as a dependency of an allowlisted package). `[extras]` *are* honored — `qiskit-addon-aqc-tensor[quimb-jax]` is what drags `quimb` / `jax` in here. `cotengrust` is needed for memory efficiency during tensor network simulation. `qiskit-aer` is listed separately for the `fake` backend (local noisy simulation)."
    ]
   },
   {

From 1e2b2ce3998d0779631c61bb9fff11a1d48bf07e Mon Sep 17 00:00:00 2001
From: Parth Danve <parth.danve@uconn.edu>
Date: Thu, 13 Aug 2026 13:01:28 -0400
Subject: [PATCH 18/25] Show expectation values and size the hardware job for
 the control hardware

Result cells now print the expectation-value array shape with its axis
order, the first and last rows, and the 2-qubit depth saved by AQC,
instead of only labels, times, and fidelities.

The hardware example passes explicit estimator options (100
randomizations at 200 shots) rather than inheriting the 1000-randomization
default, which submits ~11,000 circuit instances and can exceed a
device's control-system instruction memory (error 6073). The section now
explains the tradeoff and links Job limits and TwirlingOptions.
---
 .../function-template-aqc-trotter.ipynb       | 84 +++++++++++++++----
 1 file changed, 66 insertions(+), 18 deletions(-)

diff --git a/docs/guides/function-template-aqc-trotter.ipynb b/docs/guides/function-template-aqc-trotter.ipynb
index f5f70cdcccb..bddd09eecf3 100644
--- a/docs/guides/function-template-aqc-trotter.ipynb
+++ b/docs/guides/function-template-aqc-trotter.ipynb
@@ -419,27 +419,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "result-code",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "observable labels: ['Z_0', 'Z_1', 'Z_2', 'Z_3', 'Z_4', 'Z_5', 'Z_6', 'Z_7']\n",
-      "times: [0.0, 0.2, 0.4, 0.6000000000000001, 0.8, 1.0, 1.2000000000000002, 1.4000000000000001, 1.6]\n",
-      "AQC fidelities: {'1': 1.0, '2': 1.0, '3': 1.0, '4': 1.0, '5': 1.0, '6': 0.9999}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
+    "import numpy as np\n",
+    "\n",
     "result = job.result()\n",
-    "print(\"observable labels:\", result[\"observable_labels\"])\n",
-    "print(\"times:\", result[\"times\"])\n",
+    "ev = np.array(result[\"expectation_values\"])\n",
+    "\n",
+    "print(\"observables:\", result[\"observable_labels\"])\n",
+    "print(\"shape:\", ev.shape, \"-> (n_times, n_observables)\")\n",
+    "print(\"first row (t = 0, the prepared state):\", np.round(ev[0], 4))\n",
+    "print(\"last row (t = t_steps * dt):\", np.round(ev[-1], 4))\n",
     "print(\n",
     "    \"AQC fidelities:\",\n",
     "    {k: round(v, 4) for k, v in result[\"metadata\"][\"aqc_fidelities\"].items()},\n",
+    ")\n",
+    "\n",
+    "# What the compression bought: 2-qubit depth at the final time step.\n",
+    "stats = result[\"metadata\"][\"circuit_stats\"][\n",
+    "    str(result[\"metadata\"][\"t_steps\"])\n",
+    "]\n",
+    "print(\n",
+    "    \"2q depth at the final step:\",\n",
+    "    stats[\"full_trotter\"][\"depth_2q\"],\n",
+    "    \"(full Trotter) ->\",\n",
+    "    stats[\"aqc_trotter\"][\"depth_2q\"],\n",
+    "    \"(AQC + Trotter)\",\n",
     ")"
    ]
   },
@@ -452,7 +460,20 @@
     "\n",
     "A function call with `backend=\"runtime\"` transpiles and executes on a real IBM Quantum processor, with the function's built-in error mitigation: dynamical decoupling (XY4), gate twirling, and twirled readout error extinction (TREX). `backend_name` selects the device; omit it and the function takes the least busy one.\n",
     "\n",
-    "Nothing about the science code changes. Only the chain length, the number of Trotter steps, and the backend differ from the simulator example."
+    "Nothing about the science code changes. Only the chain length, the number of Trotter steps, and the backend differ from the simulator example.\n",
+    "\n",
+    "### Sizing the job for the control hardware\n",
+    "\n",
+    "`estimator_options` is the input worth setting deliberately. Gate twirling builds `num_randomizations` separate randomized circuits for every PUB, and the whole job, every PUB with all of its randomizations, has to fit in the instruction memory of the QPU's classical control system. The function defaults to 1000 randomizations, so a 10-step evolution submits 11 PUBs of 1000 circuits each: roughly 11,000 circuit instances in a single job.\n",
+    "\n",
+    "Exceed what the control system holds and the job fails with [error 6073](https://ibm.biz/error_codes#6073). [Job limits](/docs/guides/job-limits) gives the thresholds and how to count against them, the main one being 26.8 million control-system instructions per qubit, applied per job rather than per PUB. Dynamical decoupling adds gates that count toward it.\n",
+    "\n",
+    "Two inputs control the size:\n",
+    "\n",
+    "- `estimator_options` sets the shot budget. Total shots is `num_randomizations * shots_per_randomization`, so you can trade randomizations against shots per randomization, keep the statistics, and still shrink the program. The cell below uses 100 randomizations at 200 shots each, which is 20,000 shots per observable and about a tenth of the circuit instances the defaults would submit. See [TwirlingOptions](/docs/api/qiskit-ibm-runtime/options-twirling-options) and [Estimator options](/docs/guides/estimator-options) for the full set of fields.\n",
+    "- `batches` splits the PUBs across that many separate runtime jobs, which is the remedy error 6073 itself suggests and why the per-job framing matters. Setting `batches=4` sends roughly three PUBs per job instead of eleven at once, and the jobs go out together in one batch so the group queues once rather than each job queueing separately.\n",
+    "\n",
+    "Remember that a supplied `estimator_options` replaces the function's defaults wholesale rather than merging into them, so dynamical decoupling and TREX are restated below to keep them switched on."
    ]
   },
   {
@@ -481,6 +502,19 @@
     "    ],\n",
     "    hamiltonian=H,\n",
     "    backend=\"runtime\",\n",
+    "    backend_name=\"ibm_marrakesh\",\n",
+    "    # Lighter than the function defaults to be able to run on Heron r2, which use 1000 twirling\n",
+    "    # randomizations. Total shots is num_randomizations *\n",
+    "    # shots_per_randomization, so this is 20,000 shots per observable.\n",
+    "    estimator_options={\n",
+    "        \"dynamical_decoupling\": {\"enable\": True, \"sequence_type\": \"XY4\"},\n",
+    "        \"twirling\": {\n",
+    "            \"enable_gates\": True,\n",
+    "            \"num_randomizations\": 100,\n",
+    "            \"shots_per_randomization\": 200,\n",
+    "        },\n",
+    "        \"resilience\": {\"measure_mitigation\": True},\n",
+    "    },\n",
     ")\n",
     "print(\"job id (save this to reconnect later):\", job.job_id)"
    ]
@@ -539,13 +573,27 @@
     "# Run this only once the status cell above reports DONE. result() blocks until\n",
     "# the job finishes, so calling it earlier just waits.\n",
     "result = job.result()\n",
-    "print(\"observable labels:\", result[\"observable_labels\"])\n",
-    "print(\"times:\", result[\"times\"])\n",
+    "ev = np.array(result[\"expectation_values\"])\n",
+    "\n",
+    "print(\"backend:\", result[\"metadata\"][\"execution_backend\"])\n",
+    "print(\"shape:\", ev.shape, \"-> (n_times, n_observables)\")\n",
+    "print(\"last row (t = t_steps * dt):\", np.round(ev[-1], 4))\n",
     "print(\n",
     "    \"AQC fidelities:\",\n",
     "    {k: round(v, 4) for k, v in result[\"metadata\"][\"aqc_fidelities\"].items()},\n",
     ")\n",
-    "print(\"resource usage:\", result[\"metadata\"][\"resource_usage\"])"
+    "\n",
+    "# What the compression bought: 2-qubit depth at the final time step.\n",
+    "stats = result[\"metadata\"][\"circuit_stats\"][\n",
+    "    str(result[\"metadata\"][\"t_steps\"])\n",
+    "]\n",
+    "print(\n",
+    "    \"2q depth at the final step:\",\n",
+    "    stats[\"full_trotter\"][\"depth_2q\"],\n",
+    "    \"(full Trotter) ->\",\n",
+    "    stats[\"aqc_trotter\"][\"depth_2q\"],\n",
+    "    \"(AQC + Trotter)\",\n",
+    ")"
    ]
   },
   {

From 1d73c91a4e2a8a57ec7660b48b0f5cf721e83767 Mon Sep 17 00:00:00 2001
From: Parth Danve <parth.danve@uconn.edu>
Date: Thu, 13 Aug 2026 13:53:41 -0400
Subject: [PATCH 19/25] Add simulator and hardware outputs to the template
 notebook

Captures the results for both examples: expectation-value shape and rows,
AQC fidelities, the 2-qubit depth comparison, and the QPU time from the
hardware run.
---
 .../function-template-aqc-trotter.ipynb       | 55 ++++++++++++++++---
 1 file changed, 48 insertions(+), 7 deletions(-)

diff --git a/docs/guides/function-template-aqc-trotter.ipynb b/docs/guides/function-template-aqc-trotter.ipynb
index bddd09eecf3..d2e4b630b4c 100644
--- a/docs/guides/function-template-aqc-trotter.ipynb
+++ b/docs/guides/function-template-aqc-trotter.ipynb
@@ -419,10 +419,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 113,
    "id": "result-code",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "observables: ['Z_0', 'Z_1', 'Z_2', 'Z_3', 'Z_4', 'Z_5', 'Z_6', 'Z_7']\n",
+      "shape: (9, 8) -> (n_times, n_observables)\n",
+      "first row (t = 0, the prepared state): [1. 1. 1. 1. 1. 1. 1. 1.]\n",
+      "last row (t = t_steps * dt): [0.1442 0.2956 0.4686 0.4877 0.4869 0.4686 0.2963 0.1441]\n",
+      "AQC fidelities: {'1': 1.0, '2': 1.0, '3': 1.0, '4': 1.0, '5': 1.0, '6': 0.9999}\n",
+      "2q depth at the final step: 210 (full Trotter) -> 79 (AQC + Trotter)\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "\n",
@@ -481,7 +494,15 @@
    "execution_count": null,
    "id": "hw-code",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "job id (save this to reconnect later): 7229a8bf-9f83-4785-8dd4-489844abc2d9\n"
+     ]
+    }
+   ],
    "source": [
     "from qiskit.quantum_info import SparsePauliOp\n",
     "\n",
@@ -554,10 +575,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 116,
    "id": "hw-status",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DONE\n"
+     ]
+    }
+   ],
    "source": [
     "# Re-run this until it reports DONE, then fetch the result below.\n",
     "print(job.status())"
@@ -565,10 +594,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 118,
    "id": "hw-result-code",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "backend: runtime\n",
+      "shape: (11, 10) -> (n_times, n_observables)\n",
+      "last row (t = t_steps * dt): [0.1504 0.1361 0.218  0.2144 0.2275 0.1783 0.1749 0.1599 0.0915 0.0922]\n",
+      "AQC fidelities: {'1': 1.0, '2': 1.0, '3': 1.0, '4': 1.0, '5': 0.9999, '6': 0.9999}\n",
+      "2q depth at the final step: 342 (full Trotter) -> 171 (AQC + Trotter)\n"
+     ]
+    }
+   ],
    "source": [
     "# Run this only once the status cell above reports DONE. result() blocks until\n",
     "# the job finishes, so calling it earlier just waits.\n",

From 1b26354471f1ac2cfaf63e790261346102d4c98d Mon Sep 17 00:00:00 2001
From: Parth Danve <parth.danve@uconn.edu>
Date: Thu, 13 Aug 2026 14:41:56 -0400
Subject: [PATCH 20/25] Address review feedback on the tutorial and template
 guide

Tutorial: apply the remaining review suggestions, correct the claim that the
function is Hamiltonian-agnostic (it accepts 1D nearest-neighbor Pauli
Hamiltonians only), trim the appendix to the figures and a caption noting the
parameters are shared across sizes and can be tuned, and standardize on
"AQC Dynamics Template" as the display name.

Guide: convert the dependency note to an Admonition, correct the output schema
to the string dict keys the JSON round-trip actually produces, name the
credentials needed for the runtime backend, mirror aqc_options across the
simulator and hardware calls, and drop the future tense in the authentication
section.

Both: "job id" -> "job ID", replace "above"/"below" as page references with
"preceding"/"following" per the style guide, and fix "e.g.", stray naming, and
punctuation nits.
---
 .../function-template-aqc-trotter.ipynb       | 74 +++++++++++--------
 ...cattering-with-a-serverless-workflow.ipynb | 71 ++++++++----------
 2 files changed, 76 insertions(+), 69 deletions(-)

diff --git a/docs/guides/function-template-aqc-trotter.ipynb b/docs/guides/function-template-aqc-trotter.ipynb
index d2e4b630b4c..881674eb768 100644
--- a/docs/guides/function-template-aqc-trotter.ipynb
+++ b/docs/guides/function-template-aqc-trotter.ipynb
@@ -99,7 +99,7 @@
     "\n",
     "The name has to be exactly `source_files`, because that is the `working_dir` Step 3 uploads.\n",
     "\n",
-    "`program.py` is the entry point the gateway invokes. Everything under `source/` is the implementation, split by stage: Hamiltonian and Trotter synthesis, AQC compression, and execution. None of it needs editing to run the examples below. Step 3 uploads the whole directory, so repeat that step whenever you change a file."
+    "`program.py` is the entry point the gateway invokes. Everything under `source/` is the implementation, split by stage: Hamiltonian and Trotter synthesis, AQC compression, and execution. None of it needs editing to run the examples that follow. Step 3 uploads the whole directory, so repeat that step whenever you change a file."
    ]
   },
   {
@@ -109,7 +109,7 @@
    "source": [
     "## 1. Authentication\n",
     "\n",
-    "Use `qiskit-ibm-catalog` to authenticate to `QiskitServerless` with your API key (token) and CRN (instance), which you can find on the [IBM Quantum&reg; Platform](https://quantum.cloud.ibm.com) dashboard. This will allow you to locally instantiate the serverless client to upload or run the selected function:\n",
+    "Use `qiskit-ibm-catalog` to authenticate to `QiskitServerless` with your API key (token) and CRN (instance), which you can find on the [IBM Quantum&reg; Platform](https://quantum.cloud.ibm.com) dashboard. With these credentials you can instantiate the serverless client locally to upload or run the selected function:\n",
     "\n",
     "```python\n",
     "from qiskit_ibm_catalog import QiskitServerless\n",
@@ -151,7 +151,11 @@
     "\n",
     "Packages the function needs on top of the managed base serverless image.\n",
     "\n",
-    "> The gateway only installs names on its allowlist ([`requirements-dynamic-dependencies.txt`](https://github.com/Qiskit/qiskit-serverless/blob/main/docker-images/requirements-dynamic-dependencies.txt)), matched by package name and pinned to the allowed version with `==`. Anything else must arrive transitively (as a dependency of an allowlisted package). `[extras]` *are* honored — `qiskit-addon-aqc-tensor[quimb-jax]` is what drags `quimb` / `jax` in here. `cotengrust` is needed for memory efficiency during tensor network simulation. `qiskit-aer` is listed separately for the `fake` backend (local noisy simulation)."
+    "<Admonition type=\"note\">\n",
+    "\n",
+    "The gateway only installs names on its allowlist ([`requirements-dynamic-dependencies.txt`](https://github.com/Qiskit/qiskit-serverless/blob/main/docker-images/requirements-dynamic-dependencies.txt)), matched by package name and pinned to the allowed version with `==`. Anything else must arrive transitively (as a dependency of an allowlisted package). The `[extras]` syntax is honored: `qiskit-addon-aqc-tensor[quimb-jax]` is what installs `quimb` and `jax`. `cotengrust` is needed for memory efficiency during tensor network simulation. `qiskit-aer` is listed separately for the `fake` backend (local noisy simulation).\n",
+    "\n",
+    "</Admonition>"
    ]
   },
   {
@@ -181,7 +185,18 @@
    "execution_count": null,
    "id": "upload-code",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "QiskitFunction(aqc-dynamics-function)"
+      ]
+     },
+     "execution_count": 119,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from qiskit_ibm_catalog import QiskitFunction\n",
     "\n",
@@ -230,7 +245,7 @@
    "source": [
     "## Function reference\n",
     "\n",
-    "This is a brief introduction. Every field is documented in full in the [AQC Dynamics Template README](https://github.com/qiskit-community/qiskit-function-templates/blob/main/physics/aqc_trotter/README.md): the complete inputs table with its validation rules, the output fields, the execution backends, and further worked examples. What follows is the short version, enough to read the examples below."
+    "This is a brief introduction. Every field is documented in full in the [AQC Dynamics Template README](https://github.com/qiskit-community/qiskit-function-templates/blob/main/physics/aqc_trotter/README.md): the complete inputs table with its validation rules, the output fields, the execution backends, and further worked examples. What follows is the short version, enough to read the examples that follow."
    ]
   },
   {
@@ -240,7 +255,7 @@
    "source": [
     "### Inputs\n",
     "\n",
-    "Every run is a single `fn.run(...)` call. Only the first three inputs below are required: `hamiltonian`, `t_steps`, and `aqc_segments`. Everything after them is optional and falls back to the default shown, so a minimal call is three arguments and the rest of the table is the functionality you can opt into. The Hamiltonian's `num_qubits` sets the chain length, so there is no separate size input.\n",
+    "Every run is a single `fn.run(...)` call. Only the first three inputs in the table are required: `hamiltonian`, `t_steps`, and `aqc_segments`. Everything after them is optional and falls back to the default shown, so a minimal call is three arguments and the rest of the table is the functionality you can opt into. The Hamiltonian's `num_qubits` sets the chain length, so there is no separate size input.\n",
     "\n",
     "| Input | Default | Description |\n",
     "|---|---|---|\n",
@@ -251,7 +266,7 @@
     "| `initial_state` | `\\|0...0>` | A prepared `QuantumCircuit` to evolve. Bake any local kick into this circuit. |\n",
     "| `observables` | per-site `Z` | Anything `EstimatorV2` accepts as its `observables` argument. One observable per output column. |\n",
     "| `trotter_options` | 2nd-order Suzuki | `{\"method\": ..., \"synthesis_settings\": {...}}`. `reps` and `time` are owned by the function. |\n",
-    "| `aqc_options` | see below | `max_bond` (`32`), `cutoff` (`1e-8`), `autodiff_backend` (`\"jax\"`), `fidelity_target` (`None`), `optimizer_settings` (L-BFGS-B, `jac=True`, `maxiter=300`). |\n",
+    "| `aqc_options` | see description | `max_bond` (`32`), `cutoff` (`1e-8`), `autodiff_backend` (`\"jax\"`), `fidelity_target` (`None`), `optimizer_settings` (L-BFGS-B, `jac=True`, `maxiter=300`). |\n",
     "| `estimator_options` | DD, twirling, TREX | `EstimatorV2.options`, passed through as-is. A supplied dictionary replaces the defaults wholesale rather than merging into them. |\n",
     "| `transpiler_options` | `{\"optimization_level\": 3}` | `generate_preset_pass_manager` keyword arguments. `backend` and `target` are rejected, since the execution path owns them. |\n",
     "| `backend` | `\"runtime\"` | `\"statevector\"`, `\"fake\"`, or `\"runtime\"`. |\n",
@@ -273,9 +288,9 @@
     "|---|---|---|---|\n",
     "| `\"statevector\"` | Exact `StatevectorEstimator` | Serverless account only | The exact reference path. No QPU time. |\n",
     "| `\"fake\"` | Noisy local simulation on a Qiskit fake backend | Serverless account only | A faithful rehearsal of the mitigated `runtime` path. Needs `qiskit-aer`. Defaults to the 127-qubit `fake_sherbrooke`. |\n",
-    "| `\"runtime\"` (default) | The mitigated `EstimatorV2` against a real QPU | Yes | `backend_name` optional; omitting it selects the least busy device. |\n",
+    "| `\"runtime\"` (default) | The mitigated `EstimatorV2` against a real QPU | Serverless account and an instance with QPU access | `backend_name` optional; omitting it selects the least busy device. |\n",
     "\n",
-    "Both simulator paths still call the deployed function, so they need a saved Serverless account even though they use no QPU time. The two examples below run the same workload on `statevector` first, then on `runtime`."
+    "Both simulator paths still call the deployed function, so they need a saved Serverless account even though they use no QPU time. The two examples that follow run the same workload on `statevector` first, then on `runtime`."
    ]
   },
   {
@@ -291,24 +306,24 @@
     "{\n",
     "    \"times\": [...],                  # length t_steps + 1, t_k = k * dt (t=0 is the prepared state)\n",
     "    \"expectation_values\": [[...]],   # shape (n_times, n_observables)\n",
-    "    \"observable_labels\": [...],      # e.g. [\"Z_0\", \"ZZ_0_1\"]\n",
+    "    \"observable_labels\": [...],      # for example: [\"Z_0\", \"ZZ_0_1\"]\n",
     "    \"metadata\": {\n",
     "        \"n\", \"t_steps\", \"dt\", \"tier\",\n",
     "        \"aqc_compressed_steps\": 5,   # total compressed steps (= sum of segment n_steps)\n",
     "        \"aqc_segments\": [            # per segment: the plan plus its own results\n",
     "            {\"n_steps\": 3, \"ansatz_steps\": 1, \"steps\": [1, 2, 3], \"n_params\": 133,\n",
-    "             \"fidelities\": {1: ..., 2: ..., 3: ...}},\n",
+    "             \"fidelities\": {\"1\": ..., \"2\": ..., \"3\": ...}},\n",
     "            {\"n_steps\": 2, \"ansatz_steps\": 2, \"steps\": [4, 5], \"n_params\": 245,\n",
-    "             \"fidelities\": {4: ..., 5: ...}},\n",
+    "             \"fidelities\": {\"4\": ..., \"5\": ...}},\n",
     "        ],\n",
     "        \"execution_backend\",\n",
-    "        \"aqc_fidelities\": {1: ..., 2: ...},  # flat per-step fidelity, all compressed steps\n",
+    "        \"aqc_fidelities\": {\"1\": ..., \"2\": ...},  # flat per-step fidelity, all compressed steps\n",
     "        \"circuit_stats\": {           # per-step 2q depth and gate count, full Trotter vs AQC\n",
     "            1: {\"full_trotter\": {\"depth_2q\": ..., \"num_2q_gates\": ...},\n",
     "                \"aqc_trotter\":  {\"depth_2q\": ..., \"num_2q_gates\": ...}},\n",
     "            2: {...},\n",
     "        },\n",
-    "        \"warnings\": [...],           # non-fatal notices, e.g. a cotengrust fallback\n",
+    "        \"warnings\": [...],           # non-fatal notices; for example, a cotengrust fallback\n",
     "        \"resource_usage\": {          # per stage; QPU_TIME is the charged QPU time\n",
     "            \"RUNNING: OPTIMIZING_FOR_HARDWARE\": {\"CPU_TIME\": ...},\n",
     "            \"RUNNING: WAITING_FOR_QPU\": {\"CPU_TIME\": ...},\n",
@@ -375,7 +390,7 @@
     "    aqc_options={\"max_bond\": 32},\n",
     "    backend=\"statevector\",\n",
     ")\n",
-    "print(\"job id:\", job.job_id)"
+    "print(\"job ID:\", job.job_id)"
    ]
   },
   {
@@ -385,7 +400,7 @@
    "source": [
     "### Follow the run and read the result\n",
     "\n",
-    "`status()` reports both the coarse job lifecycle and the per-stage sub-status the function publishes as it runs. The same stages apply to the hardware run below:\n",
+    "`status()` reports both the coarse job lifecycle and the per-stage sub-status the function publishes as it runs. The same stages apply to the hardware run later in this guide:\n",
     "\n",
     "`QUEUED -> INITIALIZING -> RUNNING: OPTIMIZING_FOR_HARDWARE -> RUNNING: WAITING_FOR_QPU -> RUNNING: EXECUTING_QPU -> RUNNING: POST_PROCESSING -> DONE`\n",
     "\n",
@@ -473,7 +488,7 @@
     "\n",
     "A function call with `backend=\"runtime\"` transpiles and executes on a real IBM Quantum processor, with the function's built-in error mitigation: dynamical decoupling (XY4), gate twirling, and twirled readout error extinction (TREX). `backend_name` selects the device; omit it and the function takes the least busy one.\n",
     "\n",
-    "Nothing about the science code changes. Only the chain length, the number of Trotter steps, and the backend differ from the simulator example.\n",
+    "Nothing about the science code changes. What differs from the simulator example is the chain length, the number of Trotter steps, the compression plan, the backend, and the explicit mitigation settings covered in the following section.\n",
     "\n",
     "### Sizing the job for the control hardware\n",
     "\n",
@@ -483,10 +498,10 @@
     "\n",
     "Two inputs control the size:\n",
     "\n",
-    "- `estimator_options` sets the shot budget. Total shots is `num_randomizations * shots_per_randomization`, so you can trade randomizations against shots per randomization, keep the statistics, and still shrink the program. The cell below uses 100 randomizations at 200 shots each, which is 20,000 shots per observable and about a tenth of the circuit instances the defaults would submit. See [TwirlingOptions](/docs/api/qiskit-ibm-runtime/options-twirling-options) and [Estimator options](/docs/guides/estimator-options) for the full set of fields.\n",
+    "- `estimator_options` sets the shot budget. Total shots is `num_randomizations * shots_per_randomization`, so you can trade randomizations against shots per randomization, keep the statistics, and still shrink the program. The following cell uses 100 randomizations at 200 shots each, which is 20,000 shots per observable and about a tenth of the circuit instances the defaults would submit. See [TwirlingOptions](/docs/api/qiskit-ibm-runtime/options-twirling-options) and [Estimator options](/docs/guides/estimator-options) for the full set of fields.\n",
     "- `batches` splits the PUBs across that many separate runtime jobs, which is the remedy error 6073 itself suggests and why the per-job framing matters. Setting `batches=4` sends roughly three PUBs per job instead of eleven at once, and the jobs go out together in one batch so the group queues once rather than each job queueing separately.\n",
     "\n",
-    "Remember that a supplied `estimator_options` replaces the function's defaults wholesale rather than merging into them, so dynamical decoupling and TREX are restated below to keep them switched on."
+    "Remember that a supplied `estimator_options` replaces the function's defaults wholesale rather than merging into them, so dynamical decoupling and TREX are restated in the following cell to keep them switched on."
    ]
   },
   {
@@ -522,10 +537,11 @@
     "        {\"n_steps\": 3, \"ansatz_steps\": 2},\n",
     "    ],\n",
     "    hamiltonian=H,\n",
+    "    aqc_options={\"max_bond\": 32},\n",
     "    backend=\"runtime\",\n",
     "    backend_name=\"ibm_marrakesh\",\n",
-    "    # Lighter than the function defaults to be able to run on Heron r2, which use 1000 twirling\n",
-    "    # randomizations. Total shots is num_randomizations *\n",
+    "    # The function defaults to 1000 twirling randomizations, which was too large\n",
+    "    # for this device. Total shots is num_randomizations *\n",
     "    # shots_per_randomization, so this is 20,000 shots per observable.\n",
     "    estimator_options={\n",
     "        \"dynamical_decoupling\": {\"enable\": True, \"sequence_type\": \"XY4\"},\n",
@@ -537,7 +553,7 @@
     "        \"resilience\": {\"measure_mitigation\": True},\n",
     "    },\n",
     ")\n",
-    "print(\"job id (save this to reconnect later):\", job.job_id)"
+    "print(\"job ID (save this to reconnect later):\", job.job_id)"
    ]
   },
   {
@@ -549,13 +565,13 @@
     "\n",
     "A hardware run is not quick, and most of the time is classical rather than on the QPU. The AQC compression runs inside the function before anything reaches the QPU, and the QPU queue is on top of that. You do not need to keep this notebook or kernel open while it runs.\n",
     "\n",
-    "Copy the job ID printed above and save it. The next three cells let you pick the run back up later:\n",
+    "Copy the job ID printed by the preceding cell and save it. The next three cells let you pick the run back up later:\n",
     "\n",
     "1. Reconnect, only needed in a new kernel session: re-run the [Authentication](#1-authentication) cell to recreate `serverless`, then rebuild the `job` handle from the ID you saved. Skip this cell if you are still in the session where you submitted, because the handle is already live.\n",
     "2. Check status: re-run until it reports `DONE`.\n",
     "3. Fetch the result: run only once the status is `DONE`.\n",
     "\n",
-    "Paste your saved ID over the placeholder in the reconnect cell below.\n",
+    "Paste your saved ID over the placeholder in the following reconnect cell.\n",
     "\n",
     "</Admonition>"
    ]
@@ -569,8 +585,8 @@
    "source": [
     "# Reconnect to a previously submitted job by its ID. Only needed in a NEW kernel\n",
     "# session; if you are still in the session where you submitted, the `job` handle\n",
-    "# above is already live, so skip this cell. Replace the ID below with your own.\n",
-    "job = serverless.get_job_by_id(\"<your job id>\")"
+    "# from the preceding cell is already live, so skip this cell. Replace the ID that follows with your own.\n",
+    "job = serverless.get_job_by_id(\"<your job ID>\")"
    ]
   },
   {
@@ -588,7 +604,7 @@
     }
    ],
    "source": [
-    "# Re-run this until it reports DONE, then fetch the result below.\n",
+    "# Re-run this until it reports DONE, then fetch the result in the following cell.\n",
     "print(job.status())"
    ]
   },
@@ -611,7 +627,7 @@
     }
    ],
    "source": [
-    "# Run this only once the status cell above reports DONE. result() blocks until\n",
+    "# Run this only once the preceding status cell reports DONE. result() blocks until\n",
     "# the job finishes, so calling it earlier just waits.\n",
     "result = job.result()\n",
     "ev = np.array(result[\"expectation_values\"])\n",
@@ -647,7 +663,7 @@
     "<Admonition type=\"tip\" title=\"Recommendations\">\n",
     "\n",
     "- Work through [Simulate neutron scattering with an AQC + Trotter dynamics Serverless workflow](/docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow), the companion example that calls this deployed function to compute the dynamical structure factor of KCuF$_3$.\n",
-    "- Read the [AQC Dynamics Function Template Github](https://github.com/qiskit-community/qiskit-function-templates/blob/main/physics/aqc_trotter/) for the complete input and output contract, further examples, and citation details.\n",
+    "- Read the [AQC Dynamics Template on GitHub](https://github.com/qiskit-community/qiskit-function-templates/blob/main/physics/aqc_trotter/) for the complete input and output contract, further examples, and citation details.\n",
     "- Browse the [Qiskit Function templates repository](https://github.com/qiskit-community/qiskit-function-templates/tree/main/physics/aqc_trotter) for other application templates built the same way.\n",
     "- Read the [Qiskit Serverless guide](/docs/guides/serverless) for managing deployed functions.\n",
     "- Go deeper on the AQC compression stage with the [Qiskit addon: AQC-Tensor](https://qiskit.github.io/qiskit-addon-aqc-tensor/) documentation.\n",
diff --git a/docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow.ipynb b/docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow.ipynb
index 3613a463001..7430c3ec6c1 100644
--- a/docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow.ipynb
+++ b/docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow.ipynb
@@ -38,7 +38,7 @@
    "source": [
     "## Prerequisites\n",
     "\n",
-    "- Familiarity with [Qiskit Patterns](/docs/guides/intro-to-patterns), [`SparsePauliOp`](/docs/api/qiskit/qiskit.quantum_info.SparsePauliOp), and [Trotter time-evolution](/learning/courses/utility-scale-quantum-computing/quantum-simulation).\n",
+    "- Familiarity with [Qiskit patterns](/docs/guides/intro-to-patterns), [`SparsePauliOp`](/docs/api/qiskit/qiskit.quantum_info.SparsePauliOp), and [Trotter time-evolution](/learning/courses/utility-scale-quantum-computing/quantum-simulation).\n",
     "- Basic exposure to tensor-network methods (DMRG and MPS) is helpful but not required, as is familiarity with the [`qiskit-addon-aqc-tensor`](https://github.com/Qiskit/qiskit-addon-aqc-tensor) library that the function uses to compress Trotter circuits."
    ]
   },
@@ -49,7 +49,7 @@
    "source": [
     "## Background\n",
     "\n",
-    "Inelastic neutron scattering measures the dynamical structure factor $S(q, \\omega)$, the space-and-time Fourier transform of the spin-spin correlation function, so reproducing $S(q, \\omega)$ from a microscopic spin model is a direct, falsifiable test of a quantum simulation. This tutorial studies KCuF$_3$, a spin-$\\frac{1}{2}$ antiferromagnetic Heisenberg chain whose excitations are not single spin flips but pairs of fractionalized spinons: instead of a sharp magnon dispersion, $S(q, \\omega)$ shows a broad *two-spinon continuum*, bounded below by $\\tfrac{\\pi}{2}|\\sin q|$ and above by $\\pi|\\sin(q/2)|$. Those are the dashed curves on the plots below. The physics in full, and the comparison against measured neutron data, are covered in the [original tutorial](/docs/tutorials/simulate-neutron-scattering) and in Lee et al., [arXiv:2603.15608](https://arxiv.org/abs/2603.15608).\n",
+    "Inelastic neutron scattering measures the dynamical structure factor $S(q, \\omega)$, the space-and-time Fourier transform of the spin-spin correlation function, so reproducing $S(q, \\omega)$ from a microscopic spin model is a direct, falsifiable test of a quantum simulation. This tutorial studies KCuF$_3$, a spin-$\\frac{1}{2}$ antiferromagnetic Heisenberg chain whose excitations are not single spin flips but pairs of fractionalized spinons: instead of a sharp magnon dispersion, $S(q, \\omega)$ shows a broad *two-spinon continuum*, bounded below by $\\tfrac{\\pi}{2}|\\sin q|$ and above by $\\pi|\\sin(q/2)|$. Those are the dashed curves on the plots that follow. The physics in full, and the comparison against measured neutron data, are covered in the [original tutorial](/docs/tutorials/simulate-neutron-scattering) and in Lee et al., [arXiv:2603.15608](https://arxiv.org/abs/2603.15608).\n",
     "\n",
     "The quantum workflow mirrors the scattering experiment:\n",
     "\n",
@@ -59,7 +59,7 @@
     "4. Measure the per-site magnetization $\\langle \\sigma_z^j \\rangle(t)$; as a function of site $j$ and time $t$ this *is* the retarded Green's function $G^R(j, j_c, t)$.\n",
     "5. Fourier transform $G^R$ into $S(q, \\omega)$.\n",
     "\n",
-    "The bottleneck is step 3: exact Trotter circuits for long evolutions become too deep for hardware. Approximate quantum compilation with tensor networks (AQC) addresses this by compressing a block of Trotter steps into a fixed, shallow parameterized ansatz whose state fidelity to the exact evolution is maximized classically with an MPS simulator ([arXiv:2301.08609](https://arxiv.org/abs/2301.08609)). The [AQC Dynamics Function](/docs/guides/function-template-aqc-trotter) packages this whole quantum core (Trotter synthesis, AQC compression, and mitigated execution) behind one call:\n",
+    "Problems can arise in step 3, when exact Trotter circuits for long evolutions become too deep for hardware. AQC with tensor networks addresses this by compressing a block of Trotter steps into a fixed, shallow parameterized ansatz whose state fidelity to the exact evolution is maximized classically with an MPS simulator ([arXiv:2301.08609](https://arxiv.org/abs/2301.08609)). The [AQC Dynamics Template](/docs/guides/function-template-aqc-trotter) packages this whole quantum core (Trotter synthesis, AQC compression, and mitigated execution) behind one call:\n",
     "\n",
     "```\n",
     "   PRE (this notebook)          FUNCTION (aqc-dynamics-function)     POST (this notebook)\n",
@@ -68,9 +68,9 @@
     "                                 -> <sigma_z>(t) per site\n",
     "```\n",
     "\n",
-    "So the experiment-specific work stays here in the notebook: ground-state preparation (PRE) and the $S(q, \\omega)$ post-processing (POST). The two quantum-heavy steps, compression and execution, run inside the function.\n",
+    "The experiment-specific work stays here in the notebook: ground-state preparation (PRE) and the $S(q, \\omega)$ post-processing (POST). The two quantum-heavy steps, compression and execution, run inside the function.\n",
     "\n",
-    "This tutorial is a companion to [Simulate neutron scattering in quantum materials with quantum circuits](/docs/tutorials/simulate-neutron-scattering), which builds the same experiment inline: the same KCuF$_3$ model, ground-state preparation, neutron kick, and post-processing, with the Trotter synthesis, AQC compression, and mitigated execution written out step by step. Read that tutorial to learn how AQC compression works. Read this one to run the same experiment through a deployed function template: the quantum core becomes a single function call, and the multi-hour AQC compression runs inside the Serverless worker instead of on your machine, so you do not need an HPC system or an open kernel while it runs. Because the function is Hamiltonian-agnostic, the same call also drives other dynamics experiments."
+    "This tutorial is a companion to [Simulate neutron scattering in quantum materials with quantum circuits](/docs/tutorials/simulate-neutron-scattering), which builds the same experiment inline: the same KCuF$_3$ model, ground-state preparation, neutron kick, and post-processing, with the Trotter synthesis, AQC compression, and mitigated execution written out step by step. Read that tutorial to learn how AQC compression works. Read this one to run the same experiment through a deployed function template: the quantum core becomes a single function call, and the multi-hour AQC compression runs inside the Serverless worker instead of on your machine, so you do not need an HPC system or an open kernel while it runs. The same call also drives other 1D dynamics experiments."
    ]
   },
   {
@@ -82,9 +82,9 @@
     "\n",
     "Before starting this tutorial, be sure you have the following:\n",
     "\n",
-    "- The function deployed to your IBM Quantum&reg; Serverless account. Run the companion function template first: [Deploy and run the AQC + Trotter dynamics function template](/docs/guides/function-template-aqc-trotter). That guide walks through getting the source files and uploading the function to your account. This tutorial only calls the deployed function.\n",
+    "- The function deployed to your Qiskit Serverless account. Run the companion function template first: [Deploy and run the AQC + Trotter dynamics function template](/docs/guides/function-template-aqc-trotter). That guide walks through getting the source files and uploading the function to your account. This tutorial only calls the deployed function.\n",
     "\n",
-    "- IBM Quantum credentials saved for `QiskitServerless` (see the function template). Both examples below call the deployed function, so both need them.\n",
+    "- IBM Quantum&reg; credentials saved for `QiskitServerless` (see the function template). Both examples in this tutorial call the deployed function, so both need them.\n",
     "\n",
     "- Qiskit SDK v2.0 or later (`pip install qiskit`).\n",
     "\n",
@@ -92,7 +92,7 @@
     "\n",
     "- NumPy, SciPy, and Matplotlib (`pip install numpy scipy matplotlib`). SciPy 1.14 or later is needed for the COBYQA optimizer used in ground-state preparation.\n",
     "\n",
-    "- The AQC tensor-network stack, because the ground-state preparation in Step 1 runs locally in this notebook: `pip install 'qiskit-addon-aqc-tensor[quimb-jax]==0.3.1'`\n",
+    "- The AQC tensor-network stack, because the ground-state preparation in Step 1 runs locally in this notebook: `pip install 'qiskit-addon-aqc-tensor[quimb-jax]==0.3.1'`.\n",
     "\n",
     "\n",
     "The first call to a newly deployed function waits while the Serverless worker installs its dependencies, so expect extra latency on that run."
@@ -105,7 +105,7 @@
    "source": [
     "## Setup\n",
     "\n",
-    "Import the libraries and define the experiment-specific helpers used below: `build_gs_ansatz` (the Hamiltonian variational ansatz, or HVA, for ground-state preparation), `prepare_ground_state` (DMRG plus MPS-fidelity maximization), and `get_spectrum`, `plot_green`, and `plot_spectrum` (the $S(q, \\omega)$ post-processing). These are adapted from the [original neutron-scattering tutorial](/docs/tutorials/simulate-neutron-scattering)."
+    "Import the libraries and define the experiment-specific helpers used later: `build_gs_ansatz` (the Hamiltonian variational ansatz, or HVA, for ground-state preparation), `prepare_ground_state` (DMRG plus MPS-fidelity maximization), and `get_spectrum`, `plot_green`, and `plot_spectrum` (the $S(q, \\omega)$ post-processing). These are adapted from the [original neutron-scattering tutorial](/docs/tutorials/simulate-neutron-scattering)."
    ]
   },
   {
@@ -346,7 +346,7 @@
    "source": [
     "### Load the function template\n",
     "\n",
-    "Connect to IBM Quantum Serverless and load the deployed `aqc-dynamics-function`. Both examples below call the same `fn` handle, so the function is loaded once, here."
+    "Connect to Qiskit Serverless and load the deployed `aqc-dynamics-function`. Both examples in this tutorial call the same `fn` handle, so the function is loaded once, here."
    ]
   },
   {
@@ -368,7 +368,7 @@
    "source": [
     "## Small-scale simulator example\n",
     "\n",
-    "We first run the full workflow on a small 10-site chain using the exact `statevector` backend. This validates the PRE &rarr; FUNCTION &rarr; POST pipeline before spending any QPU time."
+    "We first run the full workflow on a small 10-site chain by using the exact `statevector` backend. This validates the PRE &rarr; FUNCTION &rarr; POST pipeline before spending any QPU time."
    ]
   },
   {
@@ -529,7 +529,7 @@
    "source": [
     "### Step 4: Post-process and return result in desired classical format\n",
     "\n",
-    "Fourier-transform the Green's function into $S(q, \\omega)$, mirror-symmetrize, and clip negatives: the standard neutron post-processing. Mirroring is exact because $S(q, \\omega) = S(-q, \\omega)$ for this model, and the negative values that survive are artifacts of Fourier-transforming a finite, discretely sampled time series, so they are clipped to zero. On this small exact run the two-spinon continuum is only coarsely resolved, but the machinery is identical to the hardware run below."
+    "Fourier-transform the Green's function into $S(q, \\omega)$, mirror-symmetrize, and clip negatives: the standard neutron post-processing. Mirroring is exact because $S(q, \\omega) = S(-q, \\omega)$ for this model, and the negative values that survive are artifacts of Fourier-transforming a finite, discretely sampled time series, so they are clipped to zero. On this small exact run the two-spinon continuum is only coarsely resolved, but the machinery is identical to the hardware run that follows."
    ]
   },
   {
@@ -594,7 +594,7 @@
     "| -------------------------------- | ------------- | ------------------------------------- |\n",
     "| Qubits                           | 10            | 30                                    |\n",
     "| Trotter steps                    | 10            | 20                                    |\n",
-    "| AQC segments (1-layer + 2-layer) | 3 + 2 = 5     | 6 + 4 = 10                            |\n",
+    "| AQC-compressed steps (1-layer + 2-layer) | 3 + 2 = 5     | 6 + 4 = 10                            |\n",
     "| Ground-state ansatz layers       | 3             | 5                                     |\n",
     "| MPS max bond dimension           | 32            | 128                                   |\n",
     "| Backend                          | `statevector` | QPU with DD, Pauli twirling, and TREX |"
@@ -609,7 +609,7 @@
     "\n",
     "Build the same KCuF$_3$ Heisenberg `SparsePauliOp` and prepare the ground state, now with a deeper `gs_layers=5` ansatz for the longer chain, then bake in the $\\pi/2$ $Z$ neutron kick at the center site. This is identical to the small-scale mapping, just at $n = 30$.\n",
     "\n",
-    "Expect a lower ground-state fidelity than the 10-site run: around 0.82 here against 0.98 above, because five HVA layers cannot fully capture a 30-site ground state. That is expected rather than a failure, and the original tutorial accepts roughly 0.65 at 50 sites for the same reason. Raising `gs_layers` or the COBYQA iteration cap improves it, at extra classical cost."
+    "Expect a lower ground-state fidelity than the 10-site run: around 0.82 here against 0.98 for the smaller chain, because five HVA layers cannot fully capture a 30-site ground state. That is expected rather than a failure, and the original tutorial accepts roughly 0.65 at 50 sites for the same reason. Raising `gs_layers` or the COBYQA iteration cap improves it, at extra classical cost."
    ]
   },
   {
@@ -725,7 +725,7 @@
     "        },\n",
     "    },\n",
     ")\n",
-    "print(\"job id (save this to reconnect later):\", job.job_id)"
+    "print(\"job ID (save this to reconnect later):\", job.job_id)"
    ]
   },
   {
@@ -735,15 +735,15 @@
    "source": [
     "<Admonition type=\"note\" title=\"Reconnecting to a long-running job\">\n",
     "\n",
-    "The large-scale run is not quick, and most of the time is classical rather than on the QPU. The AQC compression runs inside the function before anything reaches the QPU: at 30 sites with `max_bond=128` that took close to four hours in our run, against the roughly 18 minutes of QPU time quoted in the *Usage estimate* above. Queue wait is on top of both. You do not need to keep this notebook or kernel open while it runs.\n",
+    "The large-scale run is not quick, and most of the time is classical rather than on the QPU. The AQC compression runs inside the function before anything reaches the QPU: at 30 sites with `max_bond=128` that took close to four hours in our run, against the roughly 18 minutes of QPU time quoted in the *Usage estimate* at the top of this tutorial. Queue wait is on top of both. You do not need to keep this notebook or kernel open while it runs.\n",
     "\n",
-    "Copy the job id printed above and save it. The next three cells let you pick the run back up later:\n",
+    "Copy the job ID printed by the preceding cell and save it. The next three cells let you pick the run back up later:\n",
     "\n",
     "1. Reconnect, only needed in a new kernel session: re-run the [Setup](#setup) cells to recreate `serverless`, then rebuild the `job` handle from the id you saved. Skip this cell if you are still in the session where you submitted, because the handle is already live.\n",
     "2. Check status: re-run until it reports `DONE`.\n",
     "3. Fetch the result: run only once the status is `DONE`.\n",
     "\n",
-    "The reconnect cell below carries the job id from our own run. Paste yours over there:\n",
+    "The following reconnect cell holds a placeholder. Replace it with your own `job_id`:\n",
     "\n",
     "</Admonition>"
    ]
@@ -757,8 +757,8 @@
    "source": [
     "# Reconnect to a previously submitted job by its id. Only needed in a NEW kernel\n",
     "# session; if you are still in the session where you submitted, the `job` handle\n",
-    "# above is already live, so skip this cell. Replace the id below with your own.\n",
-    "job = serverless.get_job_by_id(\"<your job id>\")"
+    "# from the preceding cell is already live, so skip this cell. Replace the ID that follows with your own.\n",
+    "job = serverless.get_job_by_id(\"<your job ID>\")"
    ]
   },
   {
@@ -777,8 +777,9 @@
    ],
    "source": [
     "# Check where the job is. Re-run this until it reports DONE before fetching the\n",
-    "# result below: OPTIMIZING_FOR_HARDWARE -> WAITING_FOR_QPU -> EXECUTING_QPU ->\n",
-    "# POST_PROCESSING -> DONE.\n",
+    "# result in the following cell: QUEUED -> INITIALIZING -> RUNNING: OPTIMIZING_FOR_HARDWARE ->\n",
+    "# RUNNING: WAITING_FOR_QPU -> RUNNING: EXECUTING_QPU -> RUNNING: POST_PROCESSING\n",
+    "# -> DONE.\n",
     "print(job.status())"
    ]
   },
@@ -797,7 +798,7 @@
     }
    ],
    "source": [
-    "# Run this only once the status cell above reports DONE. result() blocks until\n",
+    "# Run this only once the preceding status cell reports DONE. result() blocks until\n",
     "# the job finishes, so calling it earlier just waits (possibly for hours).\n",
     "result = job.result()\n",
     "print(\n",
@@ -874,27 +875,17 @@
    "id": "appendix-md",
    "metadata": {},
    "source": [
-    "## Appendix: How the workflow scales\n",
+    "## Appendix\n",
     "\n",
-    "The hardware example above runs a single chain length. The three spectra below come from earlier hardware runs of this same workflow on `ibm_pittsburgh` at 10, 20, and 30 sites, with every other input held fixed: 20 Trotter steps at `dt = 0.6`, the compression plan of 6 one-layer plus 4 two-layer segments, and `max_bond = 128`. These are recorded results, not output from the cells above.\n",
+    "The preceding hardware example runs a single chain length. The three spectra that follow come from earlier hardware runs of this same workflow on `ibm_pittsburgh` at 10, 20, and 30 sites, with every other input held fixed: 20 Trotter steps at `dt = 0.6`, the compression plan of 6 one-layer plus 4 two-layer AQC-compressed steps, and `max_bond = 128`. These are recorded results, not output from the preceding cells.\n",
+    "\n",
+    "The same settings are used at all three sizes, so the spectra are directly comparable. Tuning them per chain length, with more ground-state ansatz layers or a larger `max_bond`, for example, can give better results than any shown here.\n",
     "\n",
     "![Dynamical structure factor at 10 sites, a single sharp bright peak at q = pi near the lower bound](/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/appendix-dsf-10.avif \"10 qubits\")\n",
     "\n",
     "![Dynamical structure factor at 20 sites, spectral weight filling the band between the two dashed two-spinon bounds](/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/appendix-dsf-20.avif \"20 qubits\")\n",
     "\n",
-    "![Dynamical structure factor at 30 sites, the continuum resolved more finely with fainter contrast and some weight outside the bounds](/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/appendix-dsf-30.avif \"30 qubits\")\n",
-    "\n",
-    "All three recover the two-spinon continuum, brightest at $q = \\pi$ and bounded by the dashed curves, so the physics holds at every size. What changes with chain length is a tradeoff rather than a straight improvement. Momentum resolution sharpens as $\\Delta q = 2\\pi / n$, so 30 sites map the shape of the continuum far more finely than 10 can. Signal quality moves the other way: longer chains mean deeper circuits, so noise accumulates, contrast fades, and spurious weight leaks outside the bounds.\n",
-    "\n",
-    "The two halves of the workflow scale differently in cost as well:\n",
-    "\n",
-    "| Qubits | Classical (build + AQC) | QPU usage |\n",
-    "|---|---|---|\n",
-    "| 10 | 4m 3s | 14m 21s |\n",
-    "| 20 | 24m 52s | 15m 58s |\n",
-    "| 30 | 230m 57s (about 3h 51m) | 17m 39s |\n",
-    "\n",
-    "Queue time is not counted in either column. The classical stage climbs steeply, roughly 6 times from 10 to 20 qubits and another 9 times to 30, dominated by the AQC fidelity optimization at `max_bond = 128`. QPU usage grows only about 1.2 times across the same range, because the circuit count and shot budget follow `t_steps` and the twirling settings rather than the qubit count."
+    "![Dynamical structure factor at 30 sites, the continuum resolved more finely with fainter contrast and some weight outside the bounds](/docs/images/tutorials/simulate-neutron-scattering-with-a-serverless-workflow/appendix-dsf-30.avif \"30 qubits\")"
    ]
   },
   {
@@ -906,7 +897,7 @@
     "\n",
     "<Admonition type=\"tip\" title=\"Recommendations\">\n",
     "\n",
-    "- Adapt this workflow to your own system: the function is Hamiltonian-agnostic, so a different `SparsePauliOp`, initial state, or set of observables runs the same PRE &rarr; FUNCTION &rarr; POST pipeline. See the full input/output contract in the [AQC Dynamics Template](https://github.com/qiskit-community/qiskit-function-templates/tree/main/physics/aqc_trotter).\n",
+    "- Adapt this workflow to your own system: the function accepts any 1D nearest-neighbor `SparsePauliOp`, so a different chain Hamiltonian, initial state, or set of observables runs the same PRE &rarr; FUNCTION &rarr; POST pipeline. See the full input/output contract in the [AQC Dynamics Template on GitHub](https://github.com/qiskit-community/qiskit-function-templates/tree/main/physics/aqc_trotter).\n",
     "- Read the paper this benchmark comes from: Lee et al., [*Benchmarking quantum simulation with neutron-scattering experiments*](https://arxiv.org/abs/2603.15608) (arXiv:2603.15608).\n",
     "- Compare with the [original \"Simulate neutron scattering\" tutorial](/docs/tutorials/simulate-neutron-scattering), the inline workflow this one ports onto a deployed function template.\n",
     "- Go deeper on the [error mitigation and suppression techniques](/docs/guides/error-mitigation-and-suppression-techniques) applied on the hardware run: dynamical decoupling, Pauli twirling, and TREX.\n",
@@ -916,6 +907,7 @@
   }
  ],
  "metadata": {
+  "hours": 4,
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -933,7 +925,6 @@
    "pygments_lexer": "ipython3",
    "version": "3"
   },
-  "hours": 4,
   "qpuSeconds": 1080
  },
  "nbformat": 4,

From c3e590a9cb14c74f87820b38f5fec5d133ac2eaf Mon Sep 17 00:00:00 2001
From: Parth Danve <parth.danve@uconn.edu>
Date: Thu, 13 Aug 2026 14:53:53 -0400
Subject: [PATCH 21/25] Lay out the workflow stages as a table and reword the
 Green's function step

The PRE/FUNCTION/POST diagram was ASCII art inside a code fence, so HTML
rendered a diagram as source code. Both notebooks now use a three-column
table, one column per stage, which also lets the math render instead of
sitting as plain text.

Workflow step 4 dropped the italics on "is" and states the point directly:
the per-site magnetization is exactly the retarded Green's function, so no
conversion is needed before the Fourier transform.
---
 docs/guides/function-template-aqc-trotter.ipynb       |  9 +++------
 ...eutron-scattering-with-a-serverless-workflow.ipynb | 11 ++++-------
 2 files changed, 7 insertions(+), 13 deletions(-)

diff --git a/docs/guides/function-template-aqc-trotter.ipynb b/docs/guides/function-template-aqc-trotter.ipynb
index 881674eb768..2ffdb5f58ae 100644
--- a/docs/guides/function-template-aqc-trotter.ipynb
+++ b/docs/guides/function-template-aqc-trotter.ipynb
@@ -34,12 +34,9 @@
     "\n",
     "This is an experiment-agnostic Qiskit Function template for Hamiltonian dynamics. Given a 1D nearest-neighbor Pauli Hamiltonian, a prepared initial state (optional), and a set of observables, it runs Trotter time-evolution, approximate quantum compilation (AQC) circuit compression, and mitigated execution, then returns each observable's time series. Swap the setup (PRE) and the analysis (POST) and the same core drives a different experiment:\n",
     "\n",
-    "```\n",
-    "   PRE (your setup)            FUNCTION (deployed here)             POST (your analysis)\n",
-    " prepare a state         ->  Trotter -> AQC compress -> execute  ->  S(q, w) (neutron)\n",
-    " (circuit / product)        (statevector / fake / runtime)          magnetization, transport,\n",
-    " + optional local kick      -> <O>(t)                               quench dynamics, ...\n",
-    "```\n",
+    "| PRE (your setup) | FUNCTION (deployed here) | POST (your analysis) |\n",
+    "| --- | --- | --- |\n",
+    "| Prepare a state, as a circuit or a product state, with an optional local kick | Trotter synthesis &rarr; AQC compression &rarr; execution on `statevector`, `fake`, or `runtime`, returning $\\langle O \\rangle(t)$ | $S(q, \\omega)$ for neutron scattering, or magnetization, transport, quench dynamics, and so on |\n",
     "\n",
     "The template is published in the [Qiskit Function templates repository](https://github.com/qiskit-community/qiskit-function-templates/tree/main/physics/aqc_trotter), alongside the other application templates. This notebook deploys it to your own Qiskit Serverless account. Run it once, and any notebook can then call the function with `serverless.load(\"aqc-dynamics-function\")`.\n",
     "\n",
diff --git a/docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow.ipynb b/docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow.ipynb
index 7430c3ec6c1..4633750b128 100644
--- a/docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow.ipynb
+++ b/docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow.ipynb
@@ -56,17 +56,14 @@
     "1. Prepare the chain's ground state $|\\psi_0\\rangle$.\n",
     "2. Kick it with a local perturbation at the center site, a $\\pi/2$ $Z$-rotation, mimicking the momentum and energy transfer from the neutron.\n",
     "3. Time-evolve under the Heisenberg Hamiltonian, $e^{-iHt}$, with a Trotter product formula.\n",
-    "4. Measure the per-site magnetization $\\langle \\sigma_z^j \\rangle(t)$; as a function of site $j$ and time $t$ this *is* the retarded Green's function $G^R(j, j_c, t)$.\n",
+    "4. Measure the per-site magnetization $\\langle \\sigma_z^j \\rangle(t)$. As a function of site $j$ and time $t$, this is exactly the retarded Green's function $G^R(j, j_c, t)$, so no conversion is needed before the Fourier transform in step 5.\n",
     "5. Fourier transform $G^R$ into $S(q, \\omega)$.\n",
     "\n",
     "Problems can arise in step 3, when exact Trotter circuits for long evolutions become too deep for hardware. AQC with tensor networks addresses this by compressing a block of Trotter steps into a fixed, shallow parameterized ansatz whose state fidelity to the exact evolution is maximized classically with an MPS simulator ([arXiv:2301.08609](https://arxiv.org/abs/2301.08609)). The [AQC Dynamics Template](/docs/guides/function-template-aqc-trotter) packages this whole quantum core (Trotter synthesis, AQC compression, and mitigated execution) behind one call:\n",
     "\n",
-    "```\n",
-    "   PRE (this notebook)          FUNCTION (aqc-dynamics-function)     POST (this notebook)\n",
-    " ground state (DMRG + MPS    ->  Trotter -> AQC compress -> execute -> S(q, w): the dynamical\n",
-    " fidelity max) + neutron kick    (statevector / fake / runtime)        structure factor\n",
-    "                                 -> <sigma_z>(t) per site\n",
-    "```\n",
+    "| PRE (this notebook) | FUNCTION (`aqc-dynamics-function`) | POST (this notebook) |\n",
+    "| --- | --- | --- |\n",
+    "| Ground state from DMRG plus MPS-fidelity maximization, with the neutron kick baked into the same circuit | Trotter synthesis &rarr; AQC compression &rarr; execution on `statevector`, `fake`, or `runtime`, returning $\\langle \\sigma_z^j \\rangle(t)$ per site | $S(q, \\omega)$, the dynamical structure factor |\n",
     "\n",
     "The experiment-specific work stays here in the notebook: ground-state preparation (PRE) and the $S(q, \\omega)$ post-processing (POST). The two quantum-heavy steps, compression and execution, run inside the function.\n",
     "\n",

From 19b10bbcef0623590366c8320eaf92c308366144 Mon Sep 17 00:00:00 2001
From: Parth Danve <parth.danve@uconn.edu>
Date: Thu, 13 Aug 2026 15:17:49 -0400
Subject: [PATCH 22/25] Expand state prep to state preparation in the stage
 table

---
 docs/guides/function-template-aqc-trotter.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/guides/function-template-aqc-trotter.ipynb b/docs/guides/function-template-aqc-trotter.ipynb
index 2ffdb5f58ae..eeb53a494d2 100644
--- a/docs/guides/function-template-aqc-trotter.ipynb
+++ b/docs/guides/function-template-aqc-trotter.ipynb
@@ -403,7 +403,7 @@
     "\n",
     "| `status()` value | Stage |\n",
     "|---|---|\n",
-    "| `RUNNING: OPTIMIZING_FOR_HARDWARE` | state prep, Trotter build, AQC compression |\n",
+    "| `RUNNING: OPTIMIZING_FOR_HARDWARE` | state preparation, Trotter build, AQC compression |\n",
     "| `RUNNING: WAITING_FOR_QPU` | queued on the QPU (`runtime` backend only) |\n",
     "| `RUNNING: EXECUTING_QPU` | circuits executing (local simulators mark this directly) |\n",
     "| `RUNNING: POST_PROCESSING` | assembling the result dictionary |\n",

From 69402707f0470de45ed4867e9a358ffa05fc7470 Mon Sep 17 00:00:00 2001
From: Henry Zou <henry.zou@tufts.edu>
Date: Thu, 13 Aug 2026 15:30:36 -0400
Subject: [PATCH 23/25] Apply remaining review suggestions from Abby and Henry

---
 .../function-template-aqc-trotter.ipynb       |  8 +++----
 ...cattering-with-a-serverless-workflow.ipynb | 22 +++++++++----------
 2 files changed, 15 insertions(+), 15 deletions(-)

diff --git a/docs/guides/function-template-aqc-trotter.ipynb b/docs/guides/function-template-aqc-trotter.ipynb
index eeb53a494d2..913b9d43140 100644
--- a/docs/guides/function-template-aqc-trotter.ipynb
+++ b/docs/guides/function-template-aqc-trotter.ipynb
@@ -316,9 +316,9 @@
     "        \"execution_backend\",\n",
     "        \"aqc_fidelities\": {\"1\": ..., \"2\": ...},  # flat per-step fidelity, all compressed steps\n",
     "        \"circuit_stats\": {           # per-step 2q depth and gate count, full Trotter vs AQC\n",
-    "            1: {\"full_trotter\": {\"depth_2q\": ..., \"num_2q_gates\": ...},\n",
+    "            \"1\": {\"full_trotter\": {\"depth_2q\": ..., \"num_2q_gates\": ...},\n",
     "                \"aqc_trotter\":  {\"depth_2q\": ..., \"num_2q_gates\": ...}},\n",
-    "            2: {...},\n",
+    "            \"2\": {...},\n",
     "        },\n",
     "        \"warnings\": [...],           # non-fatal notices; for example, a cotengrust fallback\n",
     "        \"resource_usage\": {          # per stage; QPU_TIME is the charged QPU time\n",
@@ -355,7 +355,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "job id: ee1f3793-e995-427d-81d1-5924549beb38\n"
+      "job ID: ee1f3793-e995-427d-81d1-5924549beb38\n"
      ]
     }
    ],
@@ -511,7 +511,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "job id (save this to reconnect later): 7229a8bf-9f83-4785-8dd4-489844abc2d9\n"
+      "job ID (save this to reconnect later): 7229a8bf-9f83-4785-8dd4-489844abc2d9\n"
      ]
     }
    ],
diff --git a/docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow.ipynb b/docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow.ipynb
index 4633750b128..1bfeccc01d2 100644
--- a/docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow.ipynb
+++ b/docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow.ipynb
@@ -23,7 +23,7 @@
    "source": [
     "## Learning outcomes\n",
     "\n",
-    "After completing this tutorial, you can expect to understand:\n",
+    "After completing this tutorial, you can expect to understand the following:\n",
     "\n",
     "- How an inelastic neutron-scattering spectrum maps to the dynamical structure factor $S(q, \\omega)$ of a 1D quantum magnet.\n",
     "- How to prepare the KCuF$_3$ (isotropic Heisenberg) ground state with the density matrix renormalization group (DMRG) and matrix product state (MPS) fidelity maximization.\n",
@@ -430,9 +430,9 @@
    "source": [
     "### Steps 2 and 3: Compress and execute with the function template\n",
     "\n",
-    "In a hand-written workflow these are two separate stages: optimize the circuits for hardware (Step 2) and execute them (Step 3). The function template collapses both into one call. It performs Trotter synthesis, AQC compression, and hardware transpilation, then runs the circuits (here on the exact simulator, later with built-in error mitigation on hardware). The two tuning parameters are `aqc_segments` (the compression plan) and `aqc_options` (the MPS and optimizer settings). Each segment `{\"n_steps\": k, \"ansatz_steps\": m}` compresses `k` consecutive Trotter steps into an ansatz built from an `m`-step Trotter target, and any steps beyond `sum(n_steps)` run as plain Trotter. Early, low-entanglement steps compress well into a shallow (`ansatz_steps=1`) ansatz, so here we compress the first 3 steps into a 1-layer ansatz and the next 2 into a deeper 2-layer ansatz; the remaining 5 of the 10 Trotter steps run as plain Trotter. For `aqc_options` we mirror the original tutorial: MPS bond dimension `max_bond=32`, `cutoff=1e-8`, and an L-BFGS-B optimizer capped at 100 iterations.\n",
+    "In a hand-written workflow these are two separate stages: optimize the circuits for hardware (Step 2) and execute them (Step 3). The function template collapses both into one call. It performs Trotter synthesis, AQC compression, and hardware transpilation, then runs the circuits (here on the exact simulator, later with built-in error mitigation on hardware). The two tuning parameters are `aqc_segments` (the compression plan) and `aqc_options` (the MPS and optimizer settings). Each segment `{\"n_steps\": k, \"ansatz_steps\": m}` compresses `k` consecutive Trotter steps into an ansatz built from an `m`-step Trotter target, and any steps beyond `sum(n_steps)` run as plain Trotter. Early, low-entanglement steps compress well into a shallow (`ansatz_steps=1`) ansatz, so here we compress the first three steps into a single-layer ansatz and the next two into a deeper two-layer ansatz; the remaining five of the 10 Trotter steps run as plain Trotter. For `aqc_options` we mirror the original tutorial: MPS bond dimension `max_bond=32`, `cutoff=1e-8`, and an L-BFGS-B optimizer capped at 100 iterations.\n",
     "\n",
-    "Call the function loaded in Setup. `backend=\"statevector\"` runs the exact reference path: no QPU time, with the circuits running on an exact statevector simulator inside the serverless worker (a saved Serverless account is still needed to call it). The `initial_state` carries the prepared ground state (including the kick); `observables` is omitted so the function measures the default per-site $Z$."
+    "Call the function loaded in Setup. `backend=\"statevector\"` runs the exact reference path: no QPU time, with the circuits running on an exact statevector simulator inside the serverless worker (a saved Qiskit Serverless account is still needed to call it). The `initial_state` carries the prepared ground state (including the kick); `observables` is omitted so the function measures the default per-site $Z$."
    ]
   },
   {
@@ -585,7 +585,7 @@
    "source": [
     "## Large-scale hardware example\n",
     "\n",
-    "The same workflow scales up without changing any of the science code: a 30-site chain, twice the Trotter depth (20 steps), a compression plan that varies the ansatz depth (a deeper ansatz for the later, more-entangled steps), and execution on a real IBM Quantum processor with the function's built-in error mitigation (dynamical decoupling, Pauli twirling, and twirled readout error extinction, or TREX). We walk through the same four steps as the simulator example, reusing the `fn` handle from Setup.\n",
+    "The same workflow scales up without changing any of the science code: a 30-site chain, twice the Trotter depth (20 steps), a compression plan that varies the ansatz depth (a deeper ansatz for the later, more-entangled steps), and execution on an IBM Quantum processor with the function's built-in error mitigation (dynamical decoupling, Pauli twirling, and twirled readout error extinction (TREX)). We walk through the same four steps as the simulator example, reusing the `fn` handle from Setup.\n",
     "\n",
     "|                                  | Small scale   | Large scale                           |\n",
     "| -------------------------------- | ------------- | ------------------------------------- |\n",
@@ -604,7 +604,7 @@
    "source": [
     "### Step 1: Map classical inputs to a quantum problem\n",
     "\n",
-    "Build the same KCuF$_3$ Heisenberg `SparsePauliOp` and prepare the ground state, now with a deeper `gs_layers=5` ansatz for the longer chain, then bake in the $\\pi/2$ $Z$ neutron kick at the center site. This is identical to the small-scale mapping, just at $n = 30$.\n",
+    "Build the same KCuF$_3$ Heisenberg `SparsePauliOp` and prepare the ground state, now with a deeper `gs_layers=5` ansatz for the longer chain, then bake in the $\\pi/2$ $Z$ neutron kick at the center site. This is identical to the small-scale mapping, but at $n = 30$.\n",
     "\n",
     "Expect a lower ground-state fidelity than the 10-site run: around 0.82 here against 0.98 for the smaller chain, because five HVA layers cannot fully capture a 30-site ground state. That is expected rather than a failure, and the original tutorial accepts roughly 0.65 at 50 sites for the same reason. Raising `gs_layers` or the COBYQA iteration cap improves it, at extra classical cost."
    ]
@@ -657,7 +657,7 @@
    "source": [
     "### Steps 2 and 3: Compress and execute with the function template\n",
     "\n",
-    "The same single call as the simulator example, now with `backend_name` pointing at a real IBM Quantum processor, so the function transpiles and executes there. The compression plan varies the ansatz depth: the first 6 (low-entanglement) Trotter steps compress into a shallow 1-layer ansatz, the next 4 into a deeper 2-layer ansatz, and the remaining 10 of the 20 steps run as plain Trotter. `aqc_options` raises the MPS bond dimension to `max_bond=128` for the longer, more-entangled chain (matching the original), keeping the same L-BFGS-B optimizer capped at 100 iterations. The `estimator_options` turn on the built-in error mitigation: dynamical decoupling (XY4), gate twirling, and TREX measurement mitigation. The function's defaults already match the original tutorial for all of these except the TREX learning budget (`measure_noise_learning`), which is the only genuine difference. The whole block is still written out because a caller-supplied `estimator_options` replaces the function's defaults wholesale instead of merging into them, so omitting a key would fall back to the Qiskit Runtime default rather than the function's."
+    "The same single call as the simulator example, now with `backend_name` pointing at an IBM Quantum processor, so the function transpiles and executes there. The compression plan varies the ansatz depth: the first six (low-entanglement) Trotter steps compress into a shallow single-layer ansatz, the next four into a deeper two-layer ansatz, and the remaining 10 of the 20 steps run as plain Trotter. `aqc_options` raises the MPS bond dimension to `max_bond=128` for the longer, more-entangled chain (matching the original), keeping the same L-BFGS-B optimizer capped at 100 iterations. The `estimator_options` turn on the built-in error mitigation: dynamical decoupling (XY4), gate twirling, and TREX measurement mitigation. The function's defaults already match the original tutorial for all of these except the TREX learning budget (`measure_noise_learning`). The whole block is still written out because a caller-supplied `estimator_options` replaces the function's defaults wholesale instead of merging into them, so omitting a key would fall back to the IBM Quantum Compute default rather than the function's."
    ]
   },
   {
@@ -670,7 +670,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "job id (save this to reconnect later): 43ed8d07-6d7d-4f33-b70a-7f31b765b310\n"
+      "job ID (save this to reconnect later): 43ed8d07-6d7d-4f33-b70a-7f31b765b310\n"
      ]
     }
    ],
@@ -736,7 +736,7 @@
     "\n",
     "Copy the job ID printed by the preceding cell and save it. The next three cells let you pick the run back up later:\n",
     "\n",
-    "1. Reconnect, only needed in a new kernel session: re-run the [Setup](#setup) cells to recreate `serverless`, then rebuild the `job` handle from the id you saved. Skip this cell if you are still in the session where you submitted, because the handle is already live.\n",
+    "1. Reconnect, only needed in a new kernel session: re-run the [Setup](#setup) cells to recreate `serverless`, then rebuild the `job` handle from the ID you saved. Skip this cell if you are still in the session where you submitted, because the handle is already live.\n",
     "2. Check status: re-run until it reports `DONE`.\n",
     "3. Fetch the result: run only once the status is `DONE`.\n",
     "\n",
@@ -752,7 +752,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Reconnect to a previously submitted job by its id. Only needed in a NEW kernel\n",
+    "# Reconnect to a previously submitted job by its ID. Only needed in a NEW kernel\n",
     "# session; if you are still in the session where you submitted, the `job` handle\n",
     "# from the preceding cell is already live, so skip this cell. Replace the ID that follows with your own.\n",
     "job = serverless.get_job_by_id(\"<your job ID>\")"
@@ -814,7 +814,7 @@
    "source": [
     "### Step 4: Post-process and return result in desired classical format\n",
     "\n",
-    "Identical post-processing to the simulator run: Fourier-transform the Green's function into $S(q, \\omega)$, mirror-symmetrize, and clip negatives. With the longer chain and evolution the two-spinon continuum is far better resolved. It should fill the band between the dashed bounds, brightest near $q = \\pi$."
+    "Identical post-processing to the simulator run: Fourier-transform the Green's function into $S(q, \\omega)$, mirror-symmetrize, and clip negatives. With the longer chain and evolution, the two-spinon continuum is far better resolved. It should fill the band between the dashed bounds, brightest near $q = \\pi$."
    ]
   },
   {
@@ -874,7 +874,7 @@
    "source": [
     "## Appendix\n",
     "\n",
-    "The preceding hardware example runs a single chain length. The three spectra that follow come from earlier hardware runs of this same workflow on `ibm_pittsburgh` at 10, 20, and 30 sites, with every other input held fixed: 20 Trotter steps at `dt = 0.6`, the compression plan of 6 one-layer plus 4 two-layer AQC-compressed steps, and `max_bond = 128`. These are recorded results, not output from the preceding cells.\n",
+    "The preceding hardware example runs a single chain length. The three spectra that follow come from earlier hardware runs of this same workflow on `ibm_pittsburgh` at 10, 20, and 30 sites, with every other input held fixed: 20 Trotter steps at `dt = 0.6`, the compression plan of six one-layer plus four two-layer AQC-compressed steps, and `max_bond = 128`. These are recorded results, not output from the preceding cells.\n",
     "\n",
     "The same settings are used at all three sizes, so the spectra are directly comparable. Tuning them per chain length, with more ground-state ansatz layers or a larger `max_bond`, for example, can give better results than any shown here.\n",
     "\n",

From 5f2dd9d89d6dc2061702aaa85b21170fae4bef4f Mon Sep 17 00:00:00 2001
From: Henry Zou <henry.zou@tufts.edu>
Date: Mon, 17 Aug 2026 12:09:02 -0400
Subject: [PATCH 24/25] Code cleanup: reuse fetched result, register template
 on hub page

---
 docs/guides/function-template-aqc-trotter.ipynb      | 12 ++++++++++--
 docs/guides/qiskit-function-templates.mdx            |  6 ++++--
 ...utron-scattering-with-a-serverless-workflow.ipynb |  4 ++--
 3 files changed, 16 insertions(+), 6 deletions(-)

diff --git a/docs/guides/function-template-aqc-trotter.ipynb b/docs/guides/function-template-aqc-trotter.ipynb
index 913b9d43140..3ad9fb9ef1e 100644
--- a/docs/guides/function-template-aqc-trotter.ipynb
+++ b/docs/guides/function-template-aqc-trotter.ipynb
@@ -530,8 +530,14 @@
     "job = fn.run(\n",
     "    t_steps=10,\n",
     "    aqc_segments=[\n",
-    "        {\"n_steps\": 3, \"ansatz_steps\": 1},\n",
-    "        {\"n_steps\": 3, \"ansatz_steps\": 2},\n",
+    "        {\n",
+    "            \"n_steps\": 3,\n",
+    "            \"ansatz_steps\": 1,\n",
+    "        },  # early steps -> shallow 1-layer ansatz\n",
+    "        {\n",
+    "            \"n_steps\": 3,\n",
+    "            \"ansatz_steps\": 2,\n",
+    "        },  # later steps -> deeper 2-layer ansatz\n",
     "    ],\n",
     "    hamiltonian=H,\n",
     "    aqc_options={\"max_bond\": 32},\n",
@@ -624,6 +630,8 @@
     }
    ],
    "source": [
+    "import numpy as np\n",
+    "\n",
     "# Run this only once the preceding status cell reports DONE. result() blocks until\n",
     "# the job finishes, so calling it earlier just waits.\n",
     "result = job.result()\n",
diff --git a/docs/guides/qiskit-function-templates.mdx b/docs/guides/qiskit-function-templates.mdx
index 6084c510cc9..8a9d30744b2 100644
--- a/docs/guides/qiskit-function-templates.mdx
+++ b/docs/guides/qiskit-function-templates.mdx
@@ -15,11 +15,12 @@ There are two types of templates:
 
 ## Template implementations
 
-Qiskit Function template implementations are organized by application area. Currently included in the collection is a physics template for Hamiltonian simulation using the [AQC-Tensor Qiskit addon](https://qiskit.github.io/qiskit-addon-aqc-tensor/) and a chemistry template for electronic structure with the implicit solvent model using the [SQD Qiskit addon](/docs/addons/qiskit-addon-sqd).
-Resources to get started with these two templates are available at the following links:
+Qiskit Function template implementations are organized by application area. Currently included in the collection are two physics templates that use the [AQC-Tensor Qiskit addon](https://qiskit.github.io/qiskit-addon-aqc-tensor/) (one for Hamiltonian simulation and one for AQC + Trotter Hamiltonian dynamics) and a chemistry template for electronic structure with the implicit solvent model using the [SQD Qiskit addon](/docs/addons/qiskit-addon-sqd).
+Resources to get started with these templates are available at the following links:
 
 - Electronic structure simulation with implicit solvent model: [template source files](https://github.com/qiskit-community/qiskit-function-templates/tree/main/chemistry/sqd_pcm) and [guide](/docs/guides/function-template-chemistry-workflow)
 - Hamiltonian simulation: [template source files](https://github.com/qiskit-community/qiskit-function-templates/tree/main/physics/hamiltonian_simulation) and [guide](/docs/guides/function-template-hamiltonian-simulation)
+- AQC + Trotter Hamiltonian dynamics: [template source files](https://github.com/qiskit-community/qiskit-function-templates/tree/main/physics/aqc_trotter) and [guide](/docs/guides/function-template-aqc-trotter)
 
 
 ## Base templates
@@ -34,6 +35,7 @@ There are currently two templates: a [circuit function template](https://github.
 
 - Review the guide on building a function template for [Hamiltonian simulation](/docs/guides/function-template-hamiltonian-simulation)
 - Read through the guide on deploying the function template for a [chemistry workflow](/docs/guides/function-template-chemistry-workflow)
+- Deploy and run the function template for [AQC + Trotter Hamiltonian dynamics](/docs/guides/function-template-aqc-trotter)
 - Check out the [Qiskit Function templates repository](https://github.com/qiskit-community/qiskit-function-templates) on GitHub.
 
 </Admonition>
diff --git a/docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow.ipynb b/docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow.ipynb
index 1bfeccc01d2..c75d5621cbd 100644
--- a/docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow.ipynb
+++ b/docs/tutorials/simulate-neutron-scattering-with-a-serverless-workflow.ipynb
@@ -474,7 +474,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "bf76d298",
+   "id": "small-status",
    "metadata": {},
    "outputs": [
     {
@@ -843,7 +843,7 @@
     }
    ],
    "source": [
-    "n = job.result()[\"metadata\"][\"n\"]\n",
+    "n = result[\"metadata\"][\"n\"]\n",
     "q_res, w_res = 100, 100\n",
     "spectrum = get_spectrum(n, Gjjc, dt, time_steps, q_res, w_res)\n",
     "spectrum = -(spectrum + spectrum[:, ::-1]) / 2  # mirror symmetry\n",

From 99aace732f280725d98a83d00a36a0e75c086ec1 Mon Sep 17 00:00:00 2001
From: Parth Danve <parth.danve@uconn.edu>
Date: Fri, 14 Aug 2026 10:49:07 -0400
Subject: [PATCH 25/25] Document the return_circuits input and the circuits
 output
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Adds the input to the Function reference table and the circuits key to the
output dict, noting it is present only when the flag is set and that
circuits[i] corresponds to times[i + 1].

Depends on qiskit-community/qiskit-function-templates#43 — do not merge this
commit before that one. Until it lands, return_circuits is not an input on the
template that this guide tells readers to download, and passing it raises a
ServerlessError (code 4615) because the input model forbids unknown fields.
---
 docs/guides/function-template-aqc-trotter.ipynb | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/docs/guides/function-template-aqc-trotter.ipynb b/docs/guides/function-template-aqc-trotter.ipynb
index 3ad9fb9ef1e..f65ec185620 100644
--- a/docs/guides/function-template-aqc-trotter.ipynb
+++ b/docs/guides/function-template-aqc-trotter.ipynb
@@ -269,7 +269,8 @@
     "| `backend` | `\"runtime\"` | `\"statevector\"`, `\"fake\"`, or `\"runtime\"`. |\n",
     "| `backend_name` | least busy | IBM&reg; backend name for `runtime`, or a named fake backend. |\n",
     "| `batches` | `1` | Split the circuits across N runtime jobs. One batch submits a single job and creates no session. |\n",
-    "| `parallel_sim` | `False` | Fan the local simulator paths across all available cores with Ray. No effect on `runtime`. |"
+    "| `parallel_sim` | `False` | Fan the local simulator paths across all available cores with Ray. No effect on `runtime`. |\n",
+    "| `return_circuits` | `False` | Return the logical AQC + Trotter circuits in the result alongside the observable series. |"
    ]
   },
   {
@@ -327,6 +328,8 @@
     "            \"RUNNING: EXECUTING_QPU\": {\"QPU_TIME\": ...},\n",
     "        },\n",
     "    },\n",
+    "    # present only when return_circuits=True\n",
+    "    \"circuits\": [QuantumCircuit, ...],  # one per evolved step; circuits[i] is at times[i + 1]\n",
     "}\n",
     "```\n",
     "\n",