diff --git a/docs/guides/_toc.json b/docs/guides/_toc.json
index 562661de2a..ebe5dc9e45 100644
--- a/docs/guides/_toc.json
+++ b/docs/guides/_toc.json
@@ -43,6 +43,31 @@
           "title": "Hello world",
           "url": "/guides/hello-world"
         },
+        {
+          "title": "Development workflow",
+          "children": [
+            {
+              "title": "Introduction to Qiskit patterns",
+              "url": "/guides/intro-to-patterns"
+            },
+            {
+              "title": "Map problem to circuits",
+              "url": "/guides/map-problem-to-circuits"
+            },
+            {
+              "title": "Optimize for hardware",
+              "url": "/guides/optimize-for-hardware"
+            },
+            {
+              "title": "Execute on hardware",
+              "url": "/guides/execute-on-hardware"
+            },
+            {
+              "title": "Post-process results",
+              "url": "/guides/post-process-results"
+            }
+          ]
+        },
         {
           "title": "Latest updates",
           "url": "/guides/latest-updates"
@@ -51,27 +76,45 @@
       "collapsible": false
     },
     {
-      "title": "Workflow",
+      "title": "Qiskit Functions",
       "children": [
         {
-          "title": "Introduction to Qiskit patterns",
-          "url": "/guides/intro-to-patterns"
+          "title": "Introduction to Qiskit Functions",
+          "url": "/guides/functions"
         },
         {
-          "title": "Map problem to circuits",
-          "url": "/guides/map-problem-to-circuits"
+        "title": "Circuit functions",
+        "children": [
+            {
+              "title": "IBM Circuit function",
+              "url": "/guides/ibm-circuit-function"
+            },
+            {
+              "title": "Algorithmiq Tensor Error Mitigation",
+              "url": "/guides/algorithmiq-tem"
+            },
+            {
+              "title": "Q-CTRL Performance Management",
+              "url": "/guides/q-ctrl-performance-management"
+            },
+            {
+              "title": "QEDMA QESEM",
+              "url": "/guides/qedma-qesem"
+            }
+          ]
         },
         {
-          "title": "Optimize for hardware",
-          "url": "/guides/optimize-for-hardware"
-        },
-        {
-          "title": "Execute on hardware",
-          "url": "/guides/execute-on-hardware"
-        },
-        {
-          "title": "Post-process results",
-          "url": "/guides/post-process-results"
+          "title": "Application functions",
+          "children": [
+              {
+                "title": "Q-CTRL Optimization Solver",
+                "url": "/guides/q-ctrl-optimization-solver"
+              },
+              {
+                "title": "QunaSys QURI Chemistry",
+                "url": "/guides/qunasys-quri-chemistry"
+              }
+            ]
         }
       ],
       "collapsible": false
@@ -477,6 +520,10 @@
             }
           ]
         },
+        {
+          "title": "Qiskit addons",
+          "url": "/guides/addons"
+        },
         {
           "title": "Qiskit Code Assistant",
           "children": [
@@ -498,4 +545,4 @@
       "collapsible": false
     }
   ]
-}
+}
\ No newline at end of file
diff --git a/docs/guides/addons.mdx b/docs/guides/addons.mdx
new file mode 100644
index 0000000000..4b29d36113
--- /dev/null
+++ b/docs/guides/addons.mdx
@@ -0,0 +1,42 @@
+---
+title: Qiskit addons
+description: Understand the Qiskit addon tools, which help you build utility-grade quantum workflows.
+---
+
+# Qiskit addons
+
+Qiskit addons are a collection of research capabilities for enabling algorithm discovery at the utility scale. These capabilities build on Qiskit's performant foundation of tools for creating and running quantum algorithms. They are provided as modular software components that can plug into a [workflow](/guides/intro-to-patterns) to scale or design new quantum algorithms.
+
+## Addons for mapping
+
+### Multi-product formulas
+
+Multi-product formulas (MPF) reduce the Trotter error of Hamiltonian dynamics through a weighted combination of several circuit executions.
+
+- Visit the [GitHub repository.](https://github.com/Qiskit/qiskit-addon-mpf)
+- Read the [documentation.](https://qiskit.github.io/qiskit-addon-mpf)
+
+## Addons for optimizing
+
+### Operator backpropagation
+
+Operator backpropagation (OBP) reduces circuit depth by trimming operations from the end at the cost of more operator measurements.
+
+- Visit the [GitHub repository.](https://github.com/Qiskit/qiskit-addon-obp)
+- Read the [documentation.](https://qiskit.github.io/qiskit-addon-obp)
+
+### Circuit cutting
+
+Circuit cutting reduces the depth of transpiled circuits by decomposing entangling gates between non-adjacent qubits.
+
+- Visit the [GitHub repository.](https://github.com/Qiskit/qiskit-addon-cutting)
+- Read the [documentation.](https://qiskit.github.io/qiskit-addon-cutting)
+
+##  Addons for post-processing
+
+### Sample-based quantum diagonalization
+
+Sample-based quantum diagonalization (SQD) classically post-processes noisy quantum samples to yield more accurate eigenvalue estimations of quantum system Hamiltonians, for example in chemistry applications.
+
+- Visit the [GitHub repository.](https://github.com/Qiskit/qiskit-addon-sqd)
+- Read the [documentation.](https://qiskit.github.io/qiskit-addon-sqd)
\ No newline at end of file
diff --git a/docs/guides/algorithmiq-tem.ipynb b/docs/guides/algorithmiq-tem.ipynb
new file mode 100644
index 0000000000..25446f6b83
--- /dev/null
+++ b/docs/guides/algorithmiq-tem.ipynb
@@ -0,0 +1,285 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f7d9993f",
+   "metadata": {},
+   "source": [
+    "{/* cspell:ignore POVM, mathbf, Filippov, Lindblad, Leahy, Rossi, García, Pérez  */}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dde95705",
+   "metadata": {},
+   "source": [
+    "# Tensor Error Mitigation (TEM): A Qiskit Function by Algorithmiq\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "The Tensor Error Mitigation (TEM) function computes expectation values of quantum circuits and observables by post-processing noise-induced errors in estimations of physical observables.\n",
+    "\n",
+    "The method consists of constructing a tensor network representing the inverse of the global noise channel affecting the state of the quantum processor, and then applying the map to informationally complete measurement outcomes obtained from the noisy state.\n",
+    "\n",
+    "As an advantage, TEM leverages informationally complete measurements to give access to a vast set of mitigated expectation values of observables and has optimal sampling\n",
+    "overhead on the quantum hardware [1]. TEM can also be advantageous with respect to purely classical tensor network methods, giving accurate results with a smaller computational cost than a classical-only tensor network approach.\n",
+    "\n",
+    "See reference [1] for further details.\n",
+    "\n",
+    "#### References\n",
+    "\n",
+    "1. S. Filippov, M. Leahy, M. A. C. Rossi, G. García-Pérez, Scalable tensor-network error mitigation for near-term quantum computing, [arXiv:1111.6950](https://arxiv.org/abs/2307.11740) [quant-ph]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f761442",
+   "metadata": {},
+   "source": [
+    "## Function description\n",
+    "\n",
+    "\n",
+    "The TEM function takes a noisy circuit with the noisy layers identified. The circuit is measured with an informationally complete positive operator-valued measure (IC-POVM), and the collected measurement outcomes are processed on a classical computer. This measurement is used to perform the tensor network methods and build a noise-inversion map $\\mathcal{N}^{-1}$. The function applies a map $\\mathcal{M}$ that fully inverts the whole noisy circuit using matrix product operators to represent the noisy layers.\n",
+    "\n",
+    "![TEM schematics](/images/guides/algorithmiq-tem/tem_scheme.svg \"Error-mitigated estimation of an observable O via post-processing measurement outcomes of the noisy quantum processor. U and N denote an ideal quantum operation and the associated noise map, which can be generally non-local (and extended to grey boxes). D stands for a tensor of operators that are dual to the effects in the IC measurement. The noise mitigation module M is a tensor network that is efficiently contracted from the middle out. The first iteration of the contraction is represented by the dotted purple line, the second one by the dashed line, and the third one by the solid line.\")\n",
+    "\n",
+    "To mitigate the noise, the algorithm performs a contraction that starts from the middle (where the inverted noisy circuit ends and the ideal circuit starts) and propagates outward by involving two layers on the left side and one layer on the right side at each iteration. Therefore, the noisy circuit output state $\\rho$ is reverted back to $(\\vert 0 \\rangle\\langle0\\vert)^{\\otimes N}$, which in turn is mapped to the ideal noiseless state operator $\\vert\\psi\\rangle\\langle \\psi\\vert$.\n",
+    "\n",
+    "The noise-mitigated estimation of an observable ${O}$ is then read as\n",
+    "$$\n",
+    "\\bar{O}_{\\text{n}.\\text{m}} = \\frac{1}{S} \\sum_{\\mathbf{k}} \\text{tr} [\\mathcal{M}(D_{\\mathbf{k}})O] = \\frac{1}{S} \\sum_{\\mathbf{k}} \\text{tr} [D_{\\mathbf{k}}\\mathcal{M}^{\\dagger}(O)],\n",
+    "$$\n",
+    "where $D\\mathbf{k}$ is the dual operator of the corresponding POVM associated with a measurement shot $\\mathbf{k}$ in a set of $S$ shots.\n",
+    "\n",
+    "\n",
+    "The algorithm is described in further detail in [Filippov et al. (2023)](https://arxiv.org/abs/2307.11740).\n",
+    "\n",
+    "\n",
+    "### Methods\n",
+    "\n",
+    "#### run\n",
+    "\n",
+    "`run(pubs,options)`\n",
+    "\n",
+    "\n",
+    "The run() method allows computing the expectation values for a primitive unified bloc (PUB) containing a circuit and a list of observables.\n",
+    "\n",
+    "**Parameters**\n",
+    "Name | Type | Description | Required | Example\n",
+    "-- | -- | -- | -- | --\n",
+    "pubs | Iterable[EstimatorPubLike] | An iterable of PUB-like (primitive unified bloc) objects, such as tuples `(circuit, observables)` or `(circuit, observables, parameter_values)`. See [Overview of PUBs](/guides/primitive-input-output#overview-of-pubs) for more information. The circuits don’t need to be ISA circuits. | Yes | (circuit, observables, parameter_values)\n",
+    "instance | str | The hub/group/project to use in that format. | No | \"hub1/group1/project1\"\n",
+    "options | dict | Input options. See `Options` section for more details. | No | \\{\"optimization_level\": 3\\}\n",
+    "\n",
+    "**Options**\n",
+    "\n",
+    "A dictionary containing the options for the TEM. The dictionary should contain the following keys:\n",
+    "\n",
+    "Name | Type | Description  | Example\n",
+    "-- | -- | -- | --\n",
+    "backend_name | str | Name of the backend to make the query.| \"ibm_fez\"\n",
+    "simulate_with_noise_model | bool | A Boolean flag indicating whether to simulate the noisy circuit or not.| False\n",
+    "max_bond_dimension | int | The maximum bond dimension to be used for MPOs. | True\n",
+    "tem_compression_cutoff | float | The cutoff value to be used for MPOs. | 1e-16\n",
+    "num_max_shots | int | The maximum number of shots. | 10_000\n",
+    "num_randomizations | int | The number of randomizations to be used for gate twirling. | 32\n",
+    "mitigate_readout_error | bool | A Boolean flag indicating whether to perform QDT readout error mitigation or not. | 24\n",
+    "num_readout_calibration_shots | int | The number of shots to be used for QDT readout error mitigation. | 1000\n",
+    "logging_level | str | The logging level to be used for the TEM runner. | \"INFO\"\n",
+    "\n",
+    "**Returns**\n",
+    "\n",
+    "A PubResult containing the TEM mitigated result:\n",
+    "\n",
+    "\n",
+    "Name |Type | Description\n",
+    "-- | -- | --\n",
+    "data | DataBin | A DataBin containing the TEM mitigated observable and its standard error. The DataBin has the following fields: <ul><li>`observable`: The TEM mitigated observable value.</li><li>`observable_stderr`: The standard error of the TEM mitigated observable.</li></ul>\n",
+    "metadadata | dict | A dictionary containing additional results. The dictionary contains the following keys: <ul><li>`\"observable_non_mitigated\"`: The observable value without error mitigation.</li><li>`\"observable_non_mitigated_stderr\"`: The standard error of the result without error mitigation.</li><li>`\"observable_simulated\"`: If its computation is enabled in the TEM options, contains the result obtained by simulating the circuit with the learned noise.</li></ul>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73390a19",
+   "metadata": {},
+   "source": [
+    "## Get started\n",
+    "\n",
+    "Authenticate using your [IBM Quantum&trade; Platform API token](http://quantum.ibm.com/), and select the TEM as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "95a715d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit_ibm_catalog import QiskitFunctionsCatalog\n",
+    "\n",
+    "tem_function_name = \"algorithmiq/tem\"\n",
+    "catalog = QiskitFunctionsCatalog(token = \"<YOUR_IQP_API_TOKEN>\")\n",
+    "\n",
+    "# Load your function\n",
+    "tem = catalog.load(tem_function_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8837f5f",
+   "metadata": {},
+   "source": [
+    "## Example\n",
+    "\n",
+    "\n",
+    "The following snippet shows an example where TEM is used to compute the expectation values of an observable given a simple quantum circuit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d56e1440",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit import QuantumCircuit\n",
+    "from qiskit.quantum_info import SparsePauliOp\n",
+    "from qiskit_ibm_runtime import QiskitRuntimeService\n",
+    "\n",
+    "# Create a quantum circuit\n",
+    "qc = QuantumCircuit(3)\n",
+    "qc.u(0.4, 0.9, -0.3, 0)\n",
+    "qc.u(-0.4, 0.2, 1.3, 1)\n",
+    "qc.u(-1.2, -1.2, 0.3, 2)\n",
+    "for _ in range(2):\n",
+    "    qc.barrier()\n",
+    "    qc.cx(0, 1)\n",
+    "    qc.cx(2, 1)\n",
+    "    qc.barrier()\n",
+    "    qc.u(0.4, 0.9, -0.3, 0)\n",
+    "    qc.u(-0.4, 0.2, 1.3, 1)\n",
+    "    qc.u(-1.2, -1.2, 0.3, 2)\n",
+    "\n",
+    "# Define the observables\n",
+    "observable = SparsePauliOp(\"YXZ\", 1.0)\n",
+    "\n",
+    "# Define the execution options\n",
+    "service = QiskitRuntimeService()\n",
+    "backend_name = service.least_busy(operational=True)\n",
+    "\n",
+    "instance = \"<IQP_HUB/IQP_GROUP/IQP_PROJECT>\"\n",
+    "\n",
+    "pub = (qc, observable)\n",
+    "options = {\n",
+    "    \"backend_name\": backend_name,\n",
+    "    \"num_max_shots\": 100,\n",
+    "}\n",
+    "job = tem.run(instance=instance, pub=pub, options=options)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03998691",
+   "metadata": {},
+   "source": [
+    "The following code checks your Qiskit Function workload's status or return results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "856fe992",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(job.status())\n",
+    "result = job.result()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e2817b13",
+   "metadata": {},
+   "source": [
+    "## Advanced options\n",
+    "\n",
+    "You can fine-tune your calculations and get more verbose information by passing additional options to TEM."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c2ce65e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "\n",
+    "options = {\n",
+    "        \"backend_name\": backend_name,\n",
+    "        \"num_max_shots\": 1_000,\n",
+    "        \"simulate_with_noise_model\": True,\n",
+    "        \"mitigate_readout_error\": True,\n",
+    "        \"num_readout_calibration_shots\": 10_000,\n",
+    "        \"logging_level\": logging.WARNING\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "job = tem.run(instance = instance, pub = pub, options = options)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e9ec2e67",
+   "metadata": {},
+   "source": [
+    "## Get support\n",
+    "\n",
+    "Reach out to [qiskit_ibm@algorithmiq.fi](mailto:qiskit_ibm@algorithmiq.fi)\n",
+    "\n",
+    "Be sure to include the following information:\n",
+    "- Qiskit Function Job ID (`qiskit-ibm-catalog`), `job.job_id`\n",
+    "- A detailed description of the issue\n",
+    "- Any relevant error messages or codes\n",
+    "- Steps to reproduce the issue"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a6a25c8",
+   "metadata": {},
+   "source": [
+    "## Next steps\n",
+    "\n",
+    "<Admonition type=\"tip\" title=\"Recommendations\">\n",
+    "\n",
+    "- [Request access to Algorithmiq Tensor Error Mitigation](https://quantum.ibm.com/functions?id=4b1b9d76-c18b-4788-b70b-15125111fbe6)\n",
+    "\n",
+    "</Admonition>"
+   ]
+  }
+ ],
+ "metadata": {
+  "description": "Introduction to TEM, a Qiskit Function by Algorithmiq, to compute estimations with software post-processing error mitigation using tensor networks.",
+  "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"
+  },
+  "title": "Tensor Error Mitigation (TEM) - A Qiskit Function by Algorithmiq"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/guides/functions.ipynb b/docs/guides/functions.ipynb
new file mode 100644
index 0000000000..62576a389d
--- /dev/null
+++ b/docs/guides/functions.ipynb
@@ -0,0 +1,320 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a47ac9aa-4bb9-4240-806e-9beabfd2dda2",
+   "metadata": {},
+   "source": [
+    "# Introduction to Qiskit Functions\n",
+    "\n",
+    "Qiskit Functions simplify and accelerate utility-scale algorithm discovery and application development, by abstracting away parts of the quantum software development workflow. In this way, Qiskit Functions free up time normally spent hand-writing code and fine-tuning experiments.\n",
+    "\n",
+    "![Overview of Qiskit Functions](/images/guides/functions/functions-overview.svg)\n",
+    "Functions come in two forms:\n",
+    "\n",
+    "| Type | What does it do? | Example inputs and outputs | Who is it for? |\n",
+    "| - | - | - | - |\n",
+    "| Circuit function | Simplified interface for running circuits. Abstracts transpilation, error suppression, error mitigation | **Input**: Abstract `PUB`s <br/> **Output**: Mitigated expectation values | Researchers using Qiskit to discover new algorithms and applications, without needing to focus on optimizing for hardware or handling error. Circuit functions can be used to build custom application functions. |\n",
+    "| Application function | Covers higher-level tasks, like exploring algorithms and domain-specific use cases. Abstracts quantum workflow to solve tasks, with classical inputs and outputs | **Input**: Molecules, graphs <br/> **Output**: Energy, cost | Researchers in non-quantum domains, integrating quantum into existing large-scale classical workflows, without needing to map classical data to quantum circuits. |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df18227b-787a-4dc6-a8c7-7abd0f67110c",
+   "metadata": {},
+   "source": [
+    "Functions are provided by IBM&reg; and third-party partners. Each is performant for specific workload characteristics and have unique performance-tuning options. Premium Plan users can get started with IBM Qiskit Functions for free, or procure a license from one of the partners who have contributed a function to the catalog.\n",
+    "\n",
+    "## Get started with Qiskit Functions\n",
+    "\n",
+    "### Install Qiskit Functions Catalog client\n",
+    "\n",
+    "To start using Qiskit Functions, install the IBM Qiskit Functions Catalog client:\n",
+    "\n",
+    "`pip install qiskit-ibm-catalog`\n",
+    "\n",
+    "Once installed, authenticate using your [IBM Quantum&trade; Platform API token](http://quantum.ibm.com/) as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "be135691-adee-4a1c-98bf-e76deb12319f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit_ibm_catalog import QiskitFunctionsCatalog\n",
+    "\n",
+    "catalog = QiskitFunctionsCatalog(token='<YOUR_IQP_API_TOKEN>')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42843fcd-1779-4a74-8d6a-7d00c22e374e",
+   "metadata": {},
+   "source": [
+    "Optionally, you can use the `save_account()` method to save your credentials for easy access later on, before initializing the service. This saves your credentials in the same place as `QiskitRuntimeService.save_account()`, so you can skip this step if you had previously used `QiskitRuntimeService` to save your account. *Note that account credentials are saved in plain text, so only do so if you are using a trusted device.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "54fc48c8-3a65-478c-9bc3-ec6eb4db425a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit_ibm_catalog import QiskitFunctionsCatalog\n",
+    "\n",
+    "# This can be skipped if you previously did QiskitRuntimeService.save_account()\n",
+    "QiskitFunctionsCatalog.save_account(token=\"<YOUR_IQP_API_TOKEN>\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5154cbea-ccfa-42ea-a9ef-c31699c3b2c0",
+   "metadata": {},
+   "source": [
+    "Once you have authenticated, you can list the functions from the Qiskit Functions Catalog that you have access to:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "55b73ad8-6e3b-4b76-8ba5-ae7ea339ffcc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[QiskitFunction(ibm/circuit-function)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "catalog.list()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1c73799-30ec-44dc-b4b9-06e59d521d8c",
+   "metadata": {},
+   "source": [
+    "### Run enabled functions\n",
+    "\n",
+    "Once a catalog object has been instantiated, you can select a function using `catalog.load(provider/function-name)`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "4ac22236-2a34-46fb-890f-30d6ba70081e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ibm_cf = catalog.load('ibm/circuit-function')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "deb844d3-051a-4db1-b0a1-4ba9801ef95e",
+   "metadata": {},
+   "source": [
+    "Each Qiskit Function has custom inputs, options, and outputs. Check the specific documentation pages for the function you want to run for more information. By default, all users can only run one function job at a time:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "9b9a7a3c-cc98-4c19-93cd-f59793515c70",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0ede4d0f-06d6-4360-86a1-5028ca511b3f\n"
+     ]
+    }
+   ],
+   "source": [
+    "job = ibm_cf.run(\n",
+    "    instance=...,\n",
+    "    pubs=[],\n",
+    "    backend=\"backend_name\"\n",
+    ")\n",
+    "\n",
+    "job.job_id"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1518e8e-04ea-4e4e-9f30-10f87dd4ee82",
+   "metadata": {},
+   "source": [
+    "### Check job status\n",
+    "\n",
+    "<Admonition type=\"tip\">\n",
+    "Currently, the IBM Quantum workloads table only reflects Qiskit Runtime workloads. Use `job.status()` to see your Qiskit Function workload's current status.\n",
+    "</Admonition>\n",
+    "\n",
+    "With your Qiskit Function `job_id`, you can check the status of running jobs. This includes the following statuses:\n",
+    "\n",
+    "- **`QUEUED`**: The remote program is in the Qiskit Function queue. The queue priority is based on how much you've used Qiskit Functions.\n",
+    "- **`INITIALIZING`**: The remote program is starting; this includes setting up the remote environment and installing dependencies.\n",
+    "- **`RUNNING`**: The program is running.\n",
+    "- **`DONE`**: The program is complete, and you can retrieve data stored in `save_result()` with `job.results()`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d811be20-cf0d-4070-a126-b40af6a33132",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "'DONE'\n"
+     ]
+    }
+   ],
+   "source": [
+    "job.status()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e6528b5-c36f-48e1-bb2f-2416ccb12902",
+   "metadata": {},
+   "source": [
+    "### Retrieve results\n",
+    "\n",
+    "Once a program is `DONE`, you can use `job.results()` to fetch the result. This output format varies with each function, so be sure to follow the specific documentation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "04f6a446-fbc6-438a-bc07-00fc9d551d83",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "PubResult(data=DataBin(evs=np.ndarray(<shape=(20,), dtype=float64>), stds=np.ndarray(<shape=(20,), dtype=float64>), evs_noise_factors=np.ndarray(<shape=(20, 3), dtype=float64>), stds_noise_factors=np.ndarray(<shape=(20, 3), dtype=float64>), ensemble_stds_noise_factors=np.ndarray(<shape=(20, 3), dtype=float64>), evs_extrapolated=np.ndarray(<shape=(20, 2, 4), dtype=float64>), stds_extrapolated=np.ndarray(<shape=(20, 2, 4), dtype=float64>), shape=(20,)), metadata={'shots': 4096, 'target_precision': 0.015625, 'circuit_metadata': {}, 'resilience': {'zne': {'extrapolator': array(['exponential', 'exponential', 'exponential', 'exponential',\n",
+      "       'exponential', 'exponential', 'exponential', 'exponential',\n",
+      "       'exponential', 'exponential', 'exponential', 'exponential',\n",
+      "       'exponential', 'exponential', 'exponential', 'exponential',\n",
+      "       'exponential', 'exponential', 'exponential', 'exponential'],\n",
+      "      dtype='<U11')}, 'layer_noise': {'noise_overhead': 1, 'total_mitigated_layers': 0, 'unique_mitigated_layers': 0, 'unique_mitigated_layers_noise_overhead': []}}, 'num_randomizations': 32})\n",
+      "      \n"
+     ]
+    }
+   ],
+   "source": [
+    "result = job.result()\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "76c32136-f449-454c-896b-ff9d0edf9abc",
+   "metadata": {},
+   "source": [
+    "At any time, you can also cancel a job:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7ddc2394-64a9-4fdd-bb3d-7729433fcd9c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "'Job has been stopped.'\n"
+     ]
+    }
+   ],
+   "source": [
+    "job.stop()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b5d7c8d-7a96-41e2-93aa-528513acdd21",
+   "metadata": {},
+   "source": [
+    "### List previously run jobs run with Qiskit Functions\n",
+    "\n",
+    "You can use `jobs()` to list all jobs submitted to Qiskit Functions:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "c1a2832b-1ca3-4ef1-9a8a-3f3804b3aedf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[<Job | 0ede4d0f-06d6-4360-86a1-5028ca511b3f>,\n",
+      " <Job | abf78e9a-b554-4e38-966a-f99cff877b8c>,\n",
+      " <Job | 90e1109e-809f-4768-a2dc-f45bf71a97b4>,\n",
+      " <Job | 313050f2-aa78-4d7d-99f4-44bdfe98e4d7>]\n"
+     ]
+    }
+   ],
+   "source": [
+    "old_jobs = catalog.jobs()\n",
+    "old_jobs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c8c3e5d-1de2-4fed-be03-d134dc3fd907",
+   "metadata": {},
+   "source": [
+    "## Next steps\n",
+    "\n",
+    "<Admonition type=\"info\" title=\"Recommendations\">\n",
+    "\n",
+    "- [Explore circuit functions](./ibm-circuit-function) to build new algorithms and applications, without needing to manage transpilation or error handling.\n",
+    "- [Explore application functions](./q-ctrl-optimization-solver) to solve domain-specific tasks, with classical inputs and outputs.\n",
+    "\n",
+    "</Admonition>"
+   ]
+  }
+ ],
+ "metadata": {
+  "description": "Explore IBM Qiskit Functions Catalog, abstracted circuit function and application function services designed to accelerate research and application prototyping.",
+  "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"
+  },
+  "title": "Introduction to Qiskit Functions"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/guides/ibm-circuit-function.ipynb b/docs/guides/ibm-circuit-function.ipynb
new file mode 100644
index 0000000000..6f6533668e
--- /dev/null
+++ b/docs/guides/ibm-circuit-function.ipynb
@@ -0,0 +1,306 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "dde95705",
+   "metadata": {},
+   "source": [
+    "# IBM Circuit function\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "The IBM&reg; Circuit function takes [abstract PUBs](./primitive-input-output) as inputs, and returns mitigated expectation values as outputs. This circuit function includes an automated and customized pipeline to enable researchers to focus on algorithm and application discovery."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f761442",
+   "metadata": {},
+   "source": [
+    "## Description\n",
+    "\n",
+    "After submitting your PUB, your abstract circuits and observables are automatically transpiled, executed on hardware, and post-processed to return mitigated expectation values. To do so, this combines the following tools:\n",
+    "\n",
+    "- [Qiskit Transpiler Service](./qiskit-transpiler-service), including auto-selection of AI-driven and heuristic transpilation passes to translate your abstract circuits to [hardware-optimized ISA circuits](/guides/optimize-for-hardware)\n",
+    "- [Error suppression and mitigation required for utility-scale computation](./error-mitigation-and-suppression-techniques), including measurement and gate twirling, dynamical decoupling, Twirled Readout Error eXtinction (TREX), Zero-Noise Extrapolation (ZNE), and Probabilistic Error Amplification (PEA)\n",
+    "- [Qiskit Runtime Estimator](./get-started-with-primitives), to execute ISA PUBs on hardware and return mitigated expectation values\n",
+    "\n",
+    "![IBM Circuit function](/images/guides/ibm-circuit-function/ibm-circuit-function.svg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73390a19",
+   "metadata": {},
+   "source": [
+    "## Get started"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f46c531c",
+   "metadata": {},
+   "source": [
+    "Authenticate using your [IBM Quantum&trade; Platform API token](http://quantum.ibm.com/), and select the Qiskit Function as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "95a715d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit_ibm_catalog import QiskitFunctionCatalog\n",
+    "\n",
+    "catalog = QiskitFunctionCatalog()\n",
+    "ibm_cf = catalog.load(\"ibm/circuit-function\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8837f5f",
+   "metadata": {},
+   "source": [
+    "## Example\n",
+    "\n",
+    " To get started, try this basic example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d56e1440",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit.circuit.random import random_circuit\n",
+    "from qiskit_ibm_runtime import QiskitRuntimeService\n",
+    "\n",
+    "# You can skip this step if you have a target backend, e.g.\n",
+    "# backend_name = \"ibm_sherbrooke\"\n",
+    "# You'll need to specify the credentials when initializing QiskitRuntimeService, if they were not previously saved.\n",
+    "service = QiskitRuntimeService()\n",
+    "backend = service.least_busy(operational=True, simulator=False)\n",
+    "\n",
+    "circuit = random_circuit(num_qubits=2, depth=2, seed=42)\n",
+    "observable = \"Z\" * circuit.num_qubits\n",
+    "pubs = [(circuit, observable)]\n",
+    "\n",
+    "job = function.run(\n",
+    "  backend=backend.name,  # Or `backend=backend_name`, if you didn't initialize a backend object\n",
+    "  pubs=pubs\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03998691",
+   "metadata": {},
+   "source": [
+    "Check your Qiskit Function workload's [status](/guides/functions#check-job-status) or return [results](/guides/functions#retrieve-results) as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "856fe992",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(job.status())\n",
+    "result = job.result()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eda36356",
+   "metadata": {},
+   "source": [
+    "The results have the same format as an [Estimator result](https://docs.quantum.ibm.com/guides/primitive-input-output#estimator-output):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "765f3207",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(f'The result of the submitted job had {len(result)} PUB and has a value:\\n {result}\\n')\n",
+    "print(f'The associated PubResult of this job has the following DataBins:\\n {result[0].data}\\n')\n",
+    "print(f'And this DataBin has attributes: {result[0].data.keys()}')\n",
+    "print(f'The expectation values measured from this PUB are: \\n{result[0].data.evs}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73815ae7",
+   "metadata": {},
+   "source": [
+    "## Parameters\n",
+    "\n",
+    "See the following table for all input parameters the IBM Circuit function accepts. The subsequent [Options](#options) section goes into more details about the available `options`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1f4e687",
+   "metadata": {},
+   "source": [
+    "| Name | Type | Description | Required | Example |\n",
+    "|-|-|-|-|-|\n",
+    "| backend | str | Name of the backend to make the query. | Yes | \"ibm_fez\" |\n",
+    "| pubs | Iterable[EstimatorPubLike]  | An iterable of PUB-like (primitive unified bloc) objects, such as tuples `(circuit, observables)` or `(circuit, observables, parameter_values)`. See [Overview of PUBs](/guides/primitive-input-output#overview-of-pubs) for more information. The circuits don’t need to be ISA circuits. | Yes | (circuit, observables, parameter_values) |\n",
+    "| precision | float | The target precision for expectation value estimates of each run Estimator PUB that does not specify its own precision.  | No | 0.1 |\n",
+    "| options | dict | Input options. See `Options` section for more details. | No | `{\"optimization_level\": 3}` |\n",
+    "| instance | str | The hub/group/project to use in that format. | No | \"hub1/group1/project1\" |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1072bd6",
+   "metadata": {},
+   "source": [
+    "### Options\n",
+    "\n",
+    "#### Structure\n",
+    "\n",
+    "Similar to Qiskit Runtime primitives, options for IBM Circuit function can be specified as a nested dictionary. Commonly used options, such as ``optimization_level`` and ``mitigation_level``, are at the first level. Other, more advanced options are grouped into different categories, such as ``resilience``.\n",
+    "\n",
+    "#### Defaults\n",
+    "\n",
+    "If you do not specify a value for an option, the default value specified by the service is used.\n",
+    "\n",
+    "#### Mitigation level\n",
+    "\n",
+    "IBM Circuit function also supports `mitigation_level`. The mitigation level specifies how much error suppression and mitigation to apply to the job. Higher levels generate more accurate results, at the expense of longer processing times. The degree of error reduction depends on the method applied. The mitigation level abstracts the detailed choice of error mitigation and suppression methods to allow users to reason about the cost/accuracy trade-off appropriate to their application. The following table shows the corresponding methods for each level.\n",
+    "\n",
+    "<Admonition type=\"note\">\n",
+    "While the names are similar, `mitigation_level` applies different techniques than the ones used by Estimator’s `resilience_level`.\n",
+    "</Admonition>\n",
+    "\n",
+    "Similar to ``resilience_level`` in Qiskit Runtime Estimator, ``mitigation_level`` specifies a base set of curated options. Any options you manually specify in addition to the mitigation level are applied on top of the base set of options defined by the mitigation level. Therefore, in principle, you could set the mitigation level to 1, but then turn off measurement mitigation, although this is not advised.\n",
+    "\n",
+    "| **Mitigation Level** | **Technique** |\n",
+    "|:-:|:-:|\n",
+    "| 1 [Default] | Dynamical decoupling + measurement twirling + TREX  |\n",
+    "| 2 | Level 1 + gate twirling + ZNE via gate folding |\n",
+    "| 3 | Level 1 + gate twirling + ZNE via PEA |\n",
+    "\n",
+    "The following example demonstrates setting the mitigation level:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "13443be4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "options = {\"mitigation_level\": 2}\n",
+    "\n",
+    "job = function.run(\n",
+    "  backend=backend.name,\n",
+    "  pubs=pubs,\n",
+    "  options=options\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c823d7b3",
+   "metadata": {},
+   "source": [
+    "#### All available options\n",
+    "\n",
+    "In addition to `mitigation_level`, the IBM Circuit function also provides a select number of advanced options that allow you to fine-tune the cost/accuracy trade-off. The following table shows all the available options:\n",
+    "\n",
+    "| Option | Sub-option | Sub-sub-option | Description | Choices | Default |\n",
+    "|-|-|-|-|-|-|\n",
+    "| default_precision |  |  | The default precision to use for any PUB or `run()`<br/>call that does not specify one.<br/>Each input PUB can specify its own precision. If the `run()` method is given a precision, then that value is used for all PUBs in the `run()` call that do not specify their own.  | float > 0 | 0.015625 |\n",
+    "| max_execution_time |  |  | Maximum execution time in seconds, which is based<br/>on QPU usage (not wall clock time). QPU usage is the<br/>amount of time that the QPU is dedicated to processing your job. If a job exceeds this time limit, it is forcibly canceled. | Integer number of seconds in the range [1, 10800] |  |\n",
+    "| mitigation_level |  |  | How much error suppression and mitigation to apply. Refer to the [Mitigation level](#mitigation-level) section for more information about the methods used at each level. | 1 / 2 / 3 | 1 |\n",
+    "| optimization_level |  |  | How much optimization to perform on the circuits. [Higher levels](/guides/set-optimization) generate more optimized circuits, at the expense of longer transpilation time. | 0 / 1 / 2 / 3 | 2 |\n",
+    "| dynamical_decoupling | enable |  | Whether to enable dynamical decoupling. Refer to [Error suppression and mitigation techniques](/guides/error-mitigation-and-suppression-techniques#dynamical-decoupling) for the explanation of the method.  | True/False | True |\n",
+    "|  | sequence_type |  | Which dynamical decoupling sequence to use.<br/>* `XX`: use the sequence `tau/2 - (+X) - tau - (+X) - tau/2`<br/>* `XpXm`: use the sequence `tau/2 - (+X) - tau - (-X) - tau/2`<br/>* `XY4`: use the sequence<br/>`tau/2 - (+X) - tau - (+Y) - tau (-X) - tau - (-Y) - tau/2` | 'XX'/'XpXm'/'XY4' | 'XX' |\n",
+    "| twirling | enable_gates |  | Whether to apply 2-qubit Clifford gate twirling. | True/False | False |\n",
+    "|  | enable_measure |  | Whether to enable twirling of measurements. | True/False | True |\n",
+    "| resilience | measure_mitigation |  | Whether to enable TREX measurement error mitigation method. Refer to [Error suppression and mitigation techniques](/guides/error-mitigation-and-suppression-techniques#twirled-readout-error-extinction-trex) for the explanation of the method.  | True/False | True |\n",
+    "|  | zne_mitigation |  | Whether to turn on Zero Noise Extrapolation error mitigation method. Refer to [Error suppression and mitigation techniques](/guides/error-mitigation-and-suppression-techniques#zero-noise-extrapolation-zne) for the explanation of the method.  | True/False | False |\n",
+    "|  | zne | amplifier | Which technique to use for amplifying noise. One of: <br/> - `gate_folding` (default) uses 2-qubit gate folding to amplify noise. If the noise factor requires amplifying only a subset of the gates, then these gates are chosen randomly.<br/><br/> - `gate_folding_front` uses 2-qubit gate folding to amplify noise. If the noise factor requires amplifying only a subset of the gates, then these gates are selected from the front of the topologically ordered DAG circuit.<br/><br/> - `gate_folding_back` uses 2-qubit gate folding to amplify noise. If the noise factor requires amplifying only a subset of the gates, then these gates are selected from the back of the topologically ordered DAG circuit.<br/><br/> - `pea` uses a technique called Probabilistic error amplification (PEA) to amplify noise. Refer to [Error suppression and mitigation techniques](/guides/error-mitigation-and-suppression-techniques#probabilistic-error-amplification-pea) for the explanation of the method.  | gate_folding / gate_folding_front / gate_folding_back / pea | gate_folding |\n",
+    "|  |  | noise_factors | Noise factors to use for noise amplification. | list of floats; each float >= 1 | (1, 1.5, 2) for PEA, and (1, 3, 5) otherwise. |\n",
+    "|  |  | extrapolator | Noise factors to evaluate the fit extrapolation models at. This option does not affect execution or model fitting in any way; it only determines the points at which the `extrapolator`s are evaluated to be returned in the data fields called `evs_extrapolated` and `stds_extrapolated`. | one or more of `exponential`,`linear`, `double_exponential`,`polynomial_degree_(1 <= k <= 7)` | (`exponential`, `linear`) |\n",
+    "|  | pec_mitigation |  | Whether to turn on Probabilistic Error Cancellation error mitigation method. Refer to [Error suppression and mitigation techniques](/guides/error-mitigation-and-suppression-techniques#probabilistic-error-cancellation-pec) for the explanation of the method.  | True/False | False |\n",
+    "|  | pec | max_overhead | The maximum circuit sampling overhead allowed, or `None` for no maximum. | None/ integer >1 | 100 |\n",
+    "\n",
+    "In the following example, setting the mitigation level to 1 initially turns off ZNE mitigation, but setting `zne_mitigation` to `True` overrides the relevent setup from `mitigation_level`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b2632bd9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "options = {\n",
+    "  \"mitigation_level\": 1,\n",
+    "  \"resilience\": {\"zne_mitigation\": True}\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "32db1269",
+   "metadata": {},
+   "source": [
+    "## Get support\n",
+    "\n",
+    "Reach out to [IBM Quantum support](https://docs.quantum.ibm.com/support), and include the following information:\n",
+    "\n",
+    "- Qiskit Function Job ID (`qiskit-ibm-catalog`), `job.job_id`\n",
+    "- A detailed description of the issue\n",
+    "- Any relevant error messages or codes\n",
+    "- Steps to reproduce the issue"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0439eef4",
+   "metadata": {},
+   "source": [
+    "## Next steps\n",
+    "\n",
+    "<Admonition type=\"tip\" title=\"Recommendations\">\n",
+    "\n",
+    "- Try the [Building workflows with the IBM Circuit function](https://learning.quantum.ibm.com/tutorial/building-workflows-with-the-ibm-circuit-function) tutorial.\n",
+    "\n",
+    "</Admonition>"
+   ]
+  }
+ ],
+ "metadata": {
+  "description": "Explore how to use the IBM Circuit function, with AI-driven transpilation and advanced error mitigation (TREX, ZNE, PEA)",
+  "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"
+  },
+  "title": "IBM Circuit function"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/guides/index.mdx b/docs/guides/index.mdx
index 2dbf27637c..d6e7a65704 100644
--- a/docs/guides/index.mdx
+++ b/docs/guides/index.mdx
@@ -46,14 +46,30 @@ Qiskit Runtime also includes three types of execution modes for running your qua
 ### Is Qiskit Runtime open-source?
 The short answer is, _not all of it_. The Qiskit Runtime service software that handles the technicalities of running your quantum program on an IBM Quantum device (including any error mitigation and suppression) is **not** open-source. However, the Qiskit Runtime client (the interface for users to access the Qiskit Runtime service), the Qiskit SDK running on the server side, and some of the software used for error mitigation, **are** open-source.  To get involved with the Qiskit open-source efforts, visit our GitHub organization at [github.com/Qiskit](https://github.com/Qiskit) and [github.com/Qiskit-Extensions](https://github.com/Qiskit-Extensions/).
 
-
 ## Qiskit Serverless
-Creating utility-scale quantum applications generally requires a variety of compute resource requirements. Premium users can use Qiskit Serverless (package name `qiskit-serverless`) to easily submit quantum workflows for remote, managed execution. See more information [here](./serverless) about how to use this collection of tools.
 
+Creating utility-scale quantum applications generally requires a variety of compute resource requirements. Qiskit Serverless (`qiskit-ibm-catalog.QiskitServerless`) provides a simple interface to run workloads across quantum-classical resources. This includes deploying programs to IBM Quantum Platform and running workloads remotely, as well as easy resource management for multi-cloud and quantum-centric supercomputing use cases. See more information in the [Qiskit Serverless documentation](/guides/serverless) about how to use this collection of tools to:
+
+- Parallelize classical tasks, such as pre-processing and post-processing
+- Persist long-running workloads in the cloud, even if your laptop is turned off
+- Deploy reusable programs in the cloud
+
+
+## Qiskit Functions
+
+Qiskit Functions (`qiskit-ibm-catalog.QiskitFunctionsCatalog`) are abstracted services designed to accelerate algorithm discovery and application prototyping. Explore the [Qiskit Functions Catalog](https://quantum.ibm.com/functions), including:
+ 
+- **Circuit functions**: Services that include transpilation, error suppression, error mitigation, and post-processing techniques that take abstract circuits and desired measurement observables as input. With Circuit functions, users can discover new algorithms and applications without needing to manage transpilation or quantum hardware performance.
+- **Application functions**: Services that include entire quantum workflows, from mapping classical to quantum, optimizing for hardware, execution on hardware, and post-processing. Users can prototype industry applications with domain-familiar inputs and outputs.
+
+Premium Plan members can access IBM-provided functions right away, or purchase licenses for the partner-provided functions directly from those partners.
 
 ## Qiskit Transpiler as a Service
 The Qiskit Transpiler Service ([package name `qiskit-ibm-transpiler`](https://pypi.org/project/qiskit-ibm-transpiler/)) is a new experimental service that provides remote transpilation capabilities on the cloud to IBM Quantum Premium Plan users. In addition to the local Qiskit SDK transpiler capabilities, your transpilation tasks can benefit from both IBM Quantum cloud resources and AI-powered transpiler passes using this service. To learn more about how to integrate cloud-based transpilation into your Qiskit workflow you can [check out the documentation](./qiskit-transpiler-service).
 
+## Qiskit addons
+
+Qiskit addons are a collection of research capabilities for utility-scale algorithm discovery. These capabilities build upon Qiskit’s performant foundation of tools for creating and running quantum algorithms. Addons are modular software components that plug into a workflow to scale or design new quantum algorithms. To learn more about the set of available Qiskit addons and how to get started using them, visit the [documentation](/guides/addons).
 
 ## The Qiskit ecosystem
 
diff --git a/docs/guides/intro-to-patterns.mdx b/docs/guides/intro-to-patterns.mdx
index 514654a536..a8942e1715 100644
--- a/docs/guides/intro-to-patterns.mdx
+++ b/docs/guides/intro-to-patterns.mdx
@@ -62,6 +62,20 @@ As the field moves from bespoke circuit construction to utility-scale workflows,
 * [Manage Qiskit Serverless compute and data resources](./serverless-manage-resources)
 * [Port code to Qiskit Serverless](./serverless-port-code)
 
+### Qiskit Functions
+
+[Qiskit Functions](/guides/functions) abstract away parts of the quantum software development workflow, and in the case of the application functions, they cover all the Qiskit patterns steps.
+
+- [Introduction to Qiskit Functions](/guides/functions)
+- Circuit functions:
+  - [IBM Circuit function](/guides/ibm-circuit-function)
+  - [Algorithmiq Tensor Error Mitigation](/guides/algorithmiq-tem)
+  - [Q-CTRL Performance Management](/guides/q-ctrl-performance-management)
+  - [QEDMA QESEM](/guides/qedma-qesem)
+- Application functions:
+  - [Q-CTRL Optimization Solver](/guides/q-ctrl-optimization-solver)
+  - [QunaSys QURI Chemistry](/guides/qunasys-quri-chemistry)
+
 ## Next steps
 
 <Admonition type="tip" title="Recommendations">
diff --git a/docs/guides/latest-updates.mdx b/docs/guides/latest-updates.mdx
index 5a4be6bca6..6d61950d1a 100644
--- a/docs/guides/latest-updates.mdx
+++ b/docs/guides/latest-updates.mdx
@@ -5,7 +5,7 @@ description: The latest updates from Qiskit and IBM Quantum, including the lates
 
 # Latest updates
 
-*Last updated 6 September 2024*
+*Last updated 16 September 2024*
 {/* remember to update the date in the previous line each time this page is updated!!! */}
 
 Keep up with the latest and greatest from Qiskit and IBM Quantum&trade;! Gathered here are the the most recent Qiskit package release summaries, documentation updates, blogs, community events, and more.
@@ -63,6 +63,12 @@ On the IBM Quantum blog, we take a deep dive into the new beta release with a sp
 
 {/* no other release notes available yet */}
 
+### Qiskit Functions Catalog Service Client v0.1.0
+
+Introducing the Qiskit Functions preview, for IBM Quantum Premium Plan users. To get started, `pip install qiskit-ibm-catalog`, and explore the [Qiskit Functions documentation](./functions). With the Qiskit Functions Catalog client, you can submit workloads to abstracted services designed to accelerate your research - sign in with your existing IBM Quantum Platform credentials.
+
+Premium Plan users can get started right away with the IBM&reg; Circuit function, or request access to circuit or application functions from our partners.
+
 ## Qiskit Runtime service updates
 
 This summary of the latest changes to the Qiskit Runtime service applies to anyone using Qiskit Runtime, regardless of how you communicate with the service (using qiskit-ibm-runtime or otherwise), or which version of the client SDK you use.
@@ -92,6 +98,34 @@ Probabilistic error amplification (PEA) error mitigation method is now available
 
 Qiskit Runtime primitives now support OpenQASM 3 as the input format for circuits and standard JSON output format. See [Qiskit Runtime REST API](/api/runtime) for examples.
 
+## Qiskit Functions Catalog updates
+
+### 16 September 2024
+
+The Qiskit Functions Catalog preview provides access to Premium Plan users to explore the available functions, including those written by IBM and those written by other members of our ecosystem. The catalog contains two kinds of functions: circuit functions and application functions.
+
+- **Circuit functions** provide a simplified interface for running circuits. They receive user-provided abstract circuits and observables as input, then manage synthesis, optimization, and execution of the representative ISA circuit. Circuit functions bring together the latest capabilities in transpilation, error suppression, and error mitigation to make utility-grade performance accessible out of the box. This allows computational scientists to focus on mapping their problems to circuits, rather than building the pattern for each problem from scratch.
+
+- **Application functions** cover higher-level tasks, like exploring algorithms and domain-specific use cases. Enterprise developers and data scientists may not have the background quantum information science knowledge for working with circuits, and instead hope to bring their domain knowledge to advance quantum computing algorithms and applications. With application functions, users can enter their classical inputs and receive solutions so they can more easily experiment with plugging quantum into their domain-specific workflows.
+
+With the launch of the Qiskit Functions Catalog, Premium Plan developers will be able to start exploring the IBM Circuit function. The IBM Circuit function includes the latest AI-powered extensions to Qiskit for circuit synthesis, optimization, and scheduling, as well as advanced error mitigation methods to return the most accurate estimations possible with today's hardware.
+
+Users can purchase licenses for the following functions contributed by our partners at Q-CTRL, QEDMA, Algorithmiq, and Qunasys.
+
+## Circuit functions
+
+- Q-CTRL is releasing a circuit function that applies AI-driven quantum control techniques, with which users can scale successfully to larger problems.
+- Algorithmiq is releasing a circuit function that applies TEM (tensor network error mitigation), an error mitigation method for obtaining estimators with fewer shots than the PEC (probabilistic error cancellation) method.
+- QEDMA is releasing a circuit function that uses proprietary protocols for efficient and accurate characterization of the noisy QPU operations, and applies error suppression and error mitigation based on the characterization data.
+
+## Application functions
+
+- QunaSys is releasing a chemistry application function comprising three algorithms meant to solve the ground state energy estimation (GSEE) problem.
+- Q-CTRL is also releasing an optimization solver with which users can pass a graph or an objective, and receive solution costs.
+
+To get started, explore the [Qiskit Functions documentation](./functions).
+
+
 ## IBM Quantum blog: Qiskit Serverless sets the stage for Qiskit Functions in the cloud
 
 *Browse all blogs at the [IBM Quantum blog page](https://www.ibm.com/quantum/blog).*
diff --git a/docs/guides/map-problem-to-circuits.mdx b/docs/guides/map-problem-to-circuits.mdx
index 0749f4ac80..e1de24c923 100644
--- a/docs/guides/map-problem-to-circuits.mdx
+++ b/docs/guides/map-problem-to-circuits.mdx
@@ -47,3 +47,10 @@ The output of this step in a Qiskit pattern is normally a collection of circuits
   * [OpenQASM 3 and the Qiskit SDK](./interoperate-qiskit-qasm3)
   * [OpenQASM 3 feature table](./qasm-feature-table)
   * [OpenQASM 3.x live specification](https://openqasm.com/)
+
+### Qiskit Functions
+
+[Qiskit Functions](/guides/functions) abstract away parts of the quantum software development workflow. These Application functions cover all the Qiskit patterns steps:
+
+- [Q-CTRL Optimization Solver](/guides/q-ctrl-optimization-solver)
+- [QunaSys QURI Chemistry](/guides/qunasys-quri-chemistry)
\ No newline at end of file
diff --git a/docs/guides/optimize-for-hardware.mdx b/docs/guides/optimize-for-hardware.mdx
index 330a5332e2..1aede89e70 100644
--- a/docs/guides/optimize-for-hardware.mdx
+++ b/docs/guides/optimize-for-hardware.mdx
@@ -52,3 +52,12 @@ can be run on IBM&reg; hardware using IBM Qiskit Runtime.
 * [Build noise models](./build-noise-models)
 * [Plot quantum states](./plot-quantum-states)
 * [Efficient simulation of stabilizer circuits with Qiskit Aer primitives](./simulate-stabilizer-circuits)
+
+### Qiskit Functions
+
+[Qiskit Functions](/guides/functions) abstract away parts of the quantum software development workflow. These Circuit functions cover both the optimize and execute steps of a Qiskit pattern:
+
+- [IBM Circuit function](/guides/ibm-circuit-function)
+- [Algorithmiq Tensor Error Mitigation](/guides/algorithmiq-tem)
+- [Q-CTRL Performance Management](/guides/q-ctrl-performance-management)
+- [QEDMA QESEM](/guides/qedma-qesem)
\ No newline at end of file
diff --git a/docs/guides/q-ctrl-optimization-solver.ipynb b/docs/guides/q-ctrl-optimization-solver.ipynb
new file mode 100644
index 0000000000..7b5235eec2
--- /dev/null
+++ b/docs/guides/q-ctrl-optimization-solver.ipynb
@@ -0,0 +1,562 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "dde95705",
+   "metadata": {},
+   "source": [
+    "# Optimization Solver: A Qiskit Function by Q-CTRL Fire Opal\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "With the Fire Opal Optimization Solver, you can solve utility-scale optimization problems on quantum hardware without requiring quantum expertise. Simply input the high-level problem definition, and the Solver takes care of the rest. The entire workflow is noise-aware and leverages [Fire Opal's Performance Management](/guides/q-ctrl-performance-management) under the hood. The Solver consistently delivers accurate solutions to classically challenging problems, even at full-device scale on the largest IBM&reg; QPUs.\n",
+    "\n",
+    "The Solver is flexible and can be used to solve combinatorial optimization problems defined as objective functions or arbitrary graphs. Problems do not have to be mapped to device topology. Both unconstrained and constrained problems are solvable, given that constraints can be formulated as penalty terms. The examples included in this guide demonstrate how to solve an unconstrained and a constrained utility-scale optimization problem using different Solver input types. The first example involves a Max-Cut problem defined on a 156-node, 3-Regular graph, while the second example tackles a 50-node Minimum Vertex Cover problem defined by a cost function.\n",
+    "\n",
+    "To get access to the Optimization Solver, [fill out this form](https://quantum.ibm.com/functions?id=a8a2b0b6-486a-41b5-8ea4-ada3b108d6b6)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f761442",
+   "metadata": {},
+   "source": [
+    "## Function description\n",
+    "\n",
+    "The Solver fully optimizes and automates the entire algorithm, from error suppression at the hardware level to efficient problem mapping and closed-loop classical optimization. Behind the scenes, the Solver's pipeline reduces errors at every stage, enabling the enhanced performance required to meaningfully scale. The underlying workflow is inspired by the Quantum Approximate Optimization Algorithm (QAOA), which is a hybrid quantum-classical algorithm. For a detailed summary of the full Optimization Solver workflow, refer to [the published manuscript](https://arxiv.org/abs/2406.01743).\n",
+    "\n",
+    "\n",
+    "![Visualization of the Optimization Solver workflow](/images/guides/qctrl-optimization/solver_workflow.svg)\n",
+    "\n",
+    "To solve a generic problem with the Optimization Solver:\n",
+    "1. Define your problem as an objective function or a graph.\n",
+    "2. Connect to the function through the Qiskit Functions Catalog.\n",
+    "3. Run the problem with the Solver and retrieve results."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d20af236",
+   "metadata": {},
+   "source": [
+    "## Inputs and outputs\n",
+    "### Inputs\n",
+    "| Name    | Type | Description | Required | Example |\n",
+    "| ---------  | ---------------- | -------------------------- | -------- | ---------- |\n",
+    "| problem  | `str`              | One of the representations listed under \"Accepted problem formats\". | Yes   |`Poly(2.0*n[0]*n[1] + 2.0*n[0]*n[2] - 3.13520241298341*n[0] + 2.0*n[1]*n[2] - 3.14469748506794*n[1] - 3.18897660121566*n[2] + 6.0, n[0], n[1], n[2], domain='RR')` |\n",
+    "| problem_type  | `str`              | Name of the problem class. This is only used for graph problem definitions to determine the objective. Currently limited to “maxcut”.       | No      | `“maxcut”`      |\n",
+    "| instance  | `str`              | The hub/group/project to use in that format.                     | Yes      | `“hub1/group1/project1”`      |\n",
+    "| run_options  | `dict`              | Input options, including the following: (Optional) `backend_name`: str. Defaults to least busy backend. | No     | `{“backend_name”: “ibm_fez\"}`     |\n",
+    "\n",
+    "**Accepted problem formats:**\n",
+    "- Polynomial expression representation of an objective function. Ideally created in Python with an existing SymPy Poly object and formatted into a string using [sympy.srepr](https://docs.sympy.org/latest/tutorials/intro-tutorial/printing.html#srepr).\n",
+    "- Graph representation of a specific problem type. The graph should be created using the networkx library in Python. It should then converted to a string by using the networkx function `[nx.readwrite.json_graph.adjacency_data](http://nx.readwrite.json_graph.adjacency_data.)`.\n",
+    "\n",
+    "### Outputs\n",
+    "| Name    | Type | Description | Example |\n",
+    "| ---------  | ---------------- | -------------------------- | -------- |\n",
+    "| result  | `dict[str, Any]`              | Solution and metadata listed under \"Result dictionary contents\"         | `{‘solution_bitstring_cost’: 3.0, ‘final_bitstring_distribution’: {‘000001’: 100, ‘000011’: 2}, ‘iteration_count’: 3, ‘solution_bitstring’: ‘000001’,  'variables_to_bitstring_index_map': {n[1]': 5, 'n[2]': 4, 'n[3]': 3, 'n[4]': 2, 'n[5]': 1}, 'best_parameters': [0.19628831763697097, -1.047052334523102], 'warnings': []}`     |\n",
+    "\n",
+    "\n",
+    "**Result dictionary contents:**\n",
+    "| Name    | Type | Description |\n",
+    "| ---------  | ---------------- | -------------------------- |\n",
+    "| solution_bitstring_cost  | `float`              | The best minimum cost across all iterations        |\n",
+    "| final_bitstring_distribution  | `CountsDict`              | The bitstring counts dictionary associated with the minimum cost        |\n",
+    "| solution_bitstring | `str`              | Bitstring with the best cost in the final distribution         |\n",
+    "| iteration_count  | `int`              | The total number of QAOA iterations performed by the Solver        |\n",
+    "| variables_to_bitstring_index_map  | `float`              | The mapping from the variables to the equivalent bit in the bitstring       |\n",
+    "| best_parameters  | `list[float]`            | The optimized beta and gamma parameters across all iterations  |\n",
+    "| warnings  |`list[str]`              | The warnings produced while compiling or running QAOA (defaults to None)   |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1165f4f0",
+   "metadata": {},
+   "source": [
+    "## Benchmarks\n",
+    "\n",
+    "[Published benchmarking results](https://arxiv.org/abs/2406.01743) show that the Solver successfully solves problems with over 120 qubits, even outperforming previously published results on quantum annealing and trapped-ion devices. The following benchmark metrics provide a rough indication of the accuracy and scaling of problem types based on a few examples. Actual metrics may differ based on various problem features, such as the number of terms in the objective function (density) and their locality, number of variables, and polynomial order.\n",
+    "\n",
+    "The \"Number of qubits\" indicated is not a hard limitation but represents rough thresholds where you can expect extremely consistent solution accuracy. Larger problem sizes have been successfully solved, and testing beyond these limits is encouraged.\n",
+    "\n",
+    "Arbitrary qubit connectivity is supported across all problem types.\n",
+    "\n",
+    "| Problem type    | Number of qubits | Example | Accuracy | Total time (s) | Runtime usage (s) | Number of iterations\n",
+    "| ---------  | ---------------- | -------------------------- | -------- | ---------- | ------------- |---- |\n",
+    "| Sparsely-connected quadratic problems  | 156 | 3-regular Max-Cut| 100%     | 1764     | 293          | 16 |\n",
+    "| Higher-order binary optimization | 156 | Ising spin-glass model | 100%      | 1461     | 272          | 16 |\n",
+    "| Densely-connected quadratic problems | 50 | Fully-connected Max-Cut| 100%      |  1758    | 268  | 12 |\n",
+    "| Constrained problem with penalty terms | 50 | Weighted Minimum Vertex Cover with 8% edge density | 100%      | 1074     | 215 | 10 |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73390a19",
+   "metadata": {},
+   "source": [
+    "## Get started\n",
+    "\n",
+    "Authenticate using your [IBM Quantum&trade; API token](http://quantum.ibm.com/), and select the Qiskit Function as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "95a715d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit_ibm_catalog import QiskitFunctionsCatalog\n",
+    "\n",
+    "# Credentials\n",
+    "token = \"<YOUR_IQP_API_TOKEN>\"\n",
+    "hub = \"<YOUR_IQP_HUB>\"\n",
+    "group = \"<YOUR_IQP_GROUP>\"\n",
+    "project = \"<YOUR_IQP_PROJECT>\"\n",
+    "\n",
+    "# Authentication\n",
+    "catalog = QiskitFunctionsCatalog(token=token)\n",
+    "\n",
+    "# Access Function\n",
+    "solver = catalog.get('q-ctrl/optimization_solver')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8837f5f",
+   "metadata": {},
+   "source": [
+    "## Example: Unconstrained Optimization\n",
+    "Run the [maximum cut](https://en.wikipedia.org/wiki/Maximum_cut) (Max-Cut) problem. The following example demonstrates the Solver's capabilities on a 120-node, 3-regular unweighted graph Max-Cut problem, but you can also solve weighted graph problems."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a699235b",
+   "metadata": {},
+   "source": [
+    "In addition to the `qiskit-serverless` package, you will also use the following packages to run this example: `NetworkX`, `PuLP`, and `NumPy`. You can install these packages by uncommenting the following cell if you are running this example in a notebook using the IPython kernel."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a74fe6a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %pip install networkx numpy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a41c4a0d",
+   "metadata": {},
+   "source": [
+    "### 1. Define the problem\n",
+    "You can run a Max-Cut problem by defining a graph problem and specifying `problem_type='maxcut'`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d56e1440",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import networkx as nx\n",
+    "import pulp\n",
+    "import numpy as np\n",
+    "\n",
+    "# Generate a random graph with 120 nodes\n",
+    "maxcut_graph = nx.random_regular_graph(d=3, n=156, seed=8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0a7255e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Optionally, visualize the graph\n",
+    "nx.draw(maxcut_graph, nx.kamada_kawai_layout(maxcut_graph), node_size=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e42a8d55",
+   "metadata": {},
+   "source": [
+    "The Solver accepts a string as the problem definition input."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "2546e3e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert graph to string\n",
+    "problem_as_str = nx.readwrite.json_graph.adjacency_data(maxcut_graph)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ab6f24f",
+   "metadata": {},
+   "source": [
+    "### 2. Run the problem\n",
+    "When using the graph-based input method, specify the problem type."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "16c66d64",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solve the problem\n",
+    "maxcut_job = solver.run(\n",
+    "    problem = problem_as_str,\n",
+    "    problem_type = \"maxcut\",\n",
+    "    instance = hub + \"/\" + group + \"/\" + project\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03998691",
+   "metadata": {},
+   "source": [
+    "You can use the familiar [Qiskit Serverless APIs](https://docs.quantum.ibm.com/guides/qiskit-serverless) to check your Qiskit Function workload's status:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "856fe992",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get job status\n",
+    "print(maxcut_job.status())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "74f48eab",
+   "metadata": {},
+   "source": [
+    "### 3. Get the result\n",
+    "Retrieve the optimal cut value from the results dictionary.\n",
+    "\n",
+    "<Admonition type=\"note\">The mapping of the variables to the bitstring may have changed. The output dictionary contains a `variables_to_bitstring_index_map` sub-dictionary, which helps to verify the ordering.</Admonition>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "6b571411",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimal cut value: 210.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Poll for results\n",
+    "maxcut_result = maxcut_job.result()\n",
+    "\n",
+    "# Take the absolute value of the solution since the cost function is minimized\n",
+    "qctrl_maxcut = abs(maxcut_result[\"solution_bitstring_cost\"])\n",
+    "\n",
+    "# Print the optimal cut value found by the Optimization Solver\n",
+    "print(f\"Optimal cut value: {qctrl_maxcut}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "508e29b0",
+   "metadata": {},
+   "source": [
+    "You can verify the accuracy of the result by solving the problem classically with open-source solvers like [PuLP](https://coin-or.github.io/pulp/) if the graph is not densely connected. High density problems may require advanced classical solvers to validate the solution."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e2817b13",
+   "metadata": {},
+   "source": [
+    "## Example: Constrained optimization\n",
+    "The prior Max-Cut example is a common quadratic unconstrained binary optimization problem. Q-CTRL's Optimization Solver can be used for various problem types, including constrained optimization. You can solve arbitrary problem types by inputting the problem definition represented as a polynomial where constraints are modeled as penalty terms.\n",
+    "\n",
+    "The following example demonstrates how to construct a cost function for a constrained optimization problem, [minimum vertex cover](https://en.wikipedia.org/wiki/Vertex_cover) (MVC)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a9fbc0e9",
+   "metadata": {},
+   "source": [
+    "In addition to the `qiskit-ibm-catalog` and `qiskit` packages, you will also use the following packages to run this example: `NumPy`, `NetworkX`, `SymPy` and `PuLP`. You can install these packages by uncommenting the following cell if you are running this example in a notebook using the IPython kernel."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c6428fd0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %pip install numpy networkx sympy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98d3bfd3",
+   "metadata": {},
+   "source": [
+    "### 1. Define the problem\n",
+    "Define a random MVC problem by generating a graph with randomly weighted nodes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "c2ce65e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import networkx as nx\n",
+    "from sympy import symbols, Poly\n",
+    "import pulp\n",
+    "\n",
+    "# To change the weights, change the seed to any integer.\n",
+    "rng_seed = 18\n",
+    "_rng = np.random.default_rng(rng_seed)\n",
+    "node_count = 50\n",
+    "edge_probability = 0.08\n",
+    "mvc_graph = nx.erdos_renyi_graph(\n",
+    "    node_count, edge_probability, seed=rng_seed, directed=False\n",
+    ")\n",
+    "\n",
+    "# add node weights\n",
+    "for i in mvc_graph.nodes:\n",
+    "    mvc_graph.add_node(i, weight=_rng.random())\n",
+    "\n",
+    "# Optionally, visualize the graph\n",
+    "nx.draw(\n",
+    "    mvc_graph,\n",
+    "    nx.kamada_kawai_layout(mvc_graph),\n",
+    "    node_size=200,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89c91472",
+   "metadata": {},
+   "source": [
+    "A standard optimization model for weighted MVC can be formulated as follows. First, a penalty must be added for any case where an edge is not connected to a vertex in the subset. Therefore, let $n_i = 1$ if vertex $i$ is in the cover (i.e., in the subset) and $n_i = 0$ otherwise. Second, the goal is to minimize the total number of vertices in the subset, which can be represented by the following function:\n",
+    "\n",
+    "$$\\textbf{Minimize}\\qquad y = \\sum_{i\\in V} \\omega_i n_i$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "91586ee9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Construct the cost function.\n",
+    "variables = symbols([f\"n[{i}]\" for i in range(node_count)])\n",
+    "cost_function = Poly(0, variables)\n",
+    "\n",
+    "for i in mvc_graph.nodes():\n",
+    "    weight = mvc_graph.nodes[i].get(\"weight\", 0)\n",
+    "    cost_function += variables[i] * weight"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d01684b",
+   "metadata": {},
+   "source": [
+    "Now every edge in the graph should include at least one end point from the cover, which can be expressed as the inequality:\n",
+    "\n",
+    "$$n_i + n_j \\ge 1 \\texttt{ for all } (i,j)\\in E$$\n",
+    "\n",
+    "Any case where an edge is not connected to the vertex of cover must be penalized. This can be represented in the cost function by adding a penalty of the form $P(1-n_i-n_j+n_i n_j)$ where $P$ is a positive penalty constant. Thus, an unconstrained alternative to the constrained inequality for weighted MVC is:\n",
+    "\n",
+    "$$\\textbf{Minimize}\\qquad y = \\sum_{i\\in V}\\omega_i n_i + P(\\sum_{(i,j)\\in E}(1 - n_i - n_j + n_i n_j))$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "1ff1ffcc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add penalty term.\n",
+    "penalty_constant = 2\n",
+    "for i, j in mvc_graph.edges():\n",
+    "    cost_function += penalty_constant * (\n",
+    "        1 - variables[i] - variables[j] + variables[i] * variables[j]\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9fd0ba9e",
+   "metadata": {},
+   "source": [
+    "### 2. Run the problem"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f380b56d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solve the problem\n",
+    "mvc_job = solver.run(\n",
+    "    problem = sympy.srepr(cost_function),\n",
+    "    instance = hub + \"/\" + group + \"/\" + project\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d154c226",
+   "metadata": {},
+   "source": [
+    "You can check your Qiskit Function workload's status as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6de77b14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(mvc_job.status())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a95618e",
+   "metadata": {},
+   "source": [
+    "### 3. Get the result\n",
+    "Retrieve the solution and analyze the results. Because this problem has weighted nodes, the solution is not simply the minimum number of nodes covered. Instead, the solution cost represents the sum of the weights of the vertices that are included in the vertex cover. It represents the total \"cost\" or \"weight\" of covering all the edges in the graph using the selected vertices."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a924bf93",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Solution cost: 10.248198273708624\n"
+     ]
+    }
+   ],
+   "source": [
+    "mvc_result = mvc_job.result()\n",
+    "qctrl_cost = mvc_result[\"solution_bitstring_cost\"]\n",
+    "\n",
+    "# Print results\n",
+    "print(f\"Solution cost: {qctrl_cost}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e9ec2e67",
+   "metadata": {},
+   "source": [
+    "## Get support\n",
+    "\n",
+    "For any questions or issues, [reach out to Q-CTRL](https://form.typeform.com/to/iuujEAEI?typeform-source=q-ctrl.com)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a6a25c8",
+   "metadata": {},
+   "source": [
+    "## Next steps\n",
+    "\n",
+    "<Admonition type=\"tip\" title=\"Recommendations\">\n",
+    "\n",
+    "- Request access to [Q-CTRL Optimization Solver](https://quantum.ibm.com/functions?id=a8a2b0b6-486a-41b5-8ea4-ada3b108d6b6)\n",
+    "- Try the [Solve higher-order binary optimization problems with Q-CTRL's Optimization Solver](https://learning.quantum.ibm.com/tutorial/solve-higher-order-binary-optimization-problems-with-q-ctrls-optimization-solver) tutorial.\n",
+    "\n",
+    "</Admonition>"
+   ]
+  }
+ ],
+ "metadata": {
+  "description": "Start solving utility-scale optimization problems with the Fire Opal Optimization Solver by Q-CTRL",
+  "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"
+  },
+  "title": "Optimization Solver - A Qiskit Function by Q-CTRL Fire Opal"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/guides/q-ctrl-performance-management.ipynb b/docs/guides/q-ctrl-performance-management.ipynb
new file mode 100644
index 0000000000..ecc10578b8
--- /dev/null
+++ b/docs/guides/q-ctrl-performance-management.ipynb
@@ -0,0 +1,545 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "dde95705",
+   "metadata": {},
+   "source": [
+    "# Performance Management: A Qiskit Function by Q-CTRL Fire Opal\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "Fire Opal Performance Management makes it simple for anyone to achieve meaningful results from quantum computers at scale without needing to be quantum hardware experts. When running circuits with Fire Opal Performance Management, the function automatically applies AI-driven error suppression techniques for successfully scaling to larger problems, using more gates and qubits on the device. At the same time, reaching the correct answer takes fewer shots and requires no overhead, meaning that you save on compute time and cost.\n",
+    "\n",
+    "Performance Management suppresses errors and increases the probability of getting the correct answer on noisy hardware. In other words, it increases the signal-to-noise ratio. The following image shows how increased accuracy enabled by Performance Management can reduce the need for additional shots in the case of a 10-qubit Quantum Fourier Transform algorithm. With only 30 shots, Q-CTRL reaches the 99% confidence threshold, whereas the default (`QiskitRuntime` Sampler, `optimization_level`=3 and `resilience_level`=1, `ibm_sherbrooke`) requires 170,000 shots. By getting the right answer faster, you save significant compute runtime.\n",
+    "\n",
+    "![Visualization of the improved runtime](/images/guides/qctrl-performance-management/achieve_more.svg)\n",
+    "\n",
+    "The Performance Management function can be used in place of the standard [Qiskit Runtime primitives](./primitives). Behind the scenes, multiple error suppression techniques work together to prevent errors from happening at runtime. All Fire Opal pipeline methods are pre-configured and algorithm-agnostic, meaning you always get the best performance out of the box.\n",
+    "\n",
+    "To get access to Performance Management, [contact Q-CTRL](https://form.typeform.com/to/JKHcaxlv?typeform-source=q-ctrl.com#product=Fire%20Opal)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f761442",
+   "metadata": {},
+   "source": [
+    "## Function description\n",
+    "\n",
+    "Fire Opal Performance Management has two options for execution that are similar to the Qiskit Runtime primitives, so you can easily swap in the Q-CTRL Sampler and Estimator. The general workflow for using the Performance Management function is:\n",
+    "1. Define your circuit (and operators in the case of the Estimator).\n",
+    "2. Run the circuit.\n",
+    "3. Retrieve the results.\n",
+    "\n",
+    "To reduce hardware noise, Fire Opal employs a range of AI-driven error suppression techniques depicted in the following image. Error suppression works at both the gate and pulse levels to address various sources of noise and to prevent the likelihood of an error occurring. With Fire Opal, the entire pipeline is completely automated with zero need for configuration. By preventing errors, the need for expensive post-processing is eliminated.\n",
+    "\n",
+    "The following image depicts the error suppression methods automated by Fire Opal Performance Management.\n",
+    "\n",
+    "![Visualization of the error suppression pipeline](/images/guides/qctrl-performance-management/error_suppression.svg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b364970b",
+   "metadata": {},
+   "source": [
+    "## Inputs and outputs\n",
+    "The Sampler and Estimator inputs are meant to closely follow the implemented spec for [Qiskit Runtime V2 primitives](../migration-guides/v2-primitives). The Q-CTRL primitives accept inputs in the form of Primitive Unified Blocs (PUBs), which are tuples containing circuits and their relevant execution data. For more information on the PUB data structure, refer to the [IBM Quantum Documentation](./primitive-input-output). The main difference is that the Q-CTRL primitives accept PUBs containing basic types - for example, QASM strings, dictionaries, and so on.\n",
+    "\n",
+    "<Admonition type=\"caution\">Fire Opal Performance Management accepts abstract circuits, in contrast to the native Qiskit Runtime primitives, which only accept circuits that are written in the target backend’s Instruction Set Architecture (ISA). For best results, do not transpile circuits before submitting via the Performance Management function. </Admonition>\n",
+    "\n",
+    "\n",
+    "### Sampler inputs\n",
+    "| Name       | Type                                          | Description                                                                                                 | Required | Example                                                                                                             |\n",
+    "|------------|-----------------------------------------------|-------------------------------------------------------------------------------------------------------------|----------|---------------------------------------------------------------------------------------------------------------------|\n",
+    "| pubs       | `SamplerPubLike` or `list[SamplerPubLike]`   | One or more tuples containing the inputs listed under `SamplerPubLike` components.                          | Yes      | `(circuit, parameter_values)`                                                                                |\n",
+    "| instance   | str                                           | The hub/group/project to use in that format.                                                               | Yes      | `\"hub1/group1/project1\"`                                                                                           |\n",
+    "| run_options| dict                                          | Input options which includes the following: (Optional) `backend_name`: str, `shots`: int. Defaults to least busy backend and 4000 shots. | No       | `{\"backend_name\": backend_name, \"shots\": 2048}`                                                                   |\n",
+    "\n",
+    "**`SamplerPubLike` components:**\n",
+    "- A single circuit in the OpenQASM 2.0 or 3.0 string format.\n",
+    "- (Optional) A collection of parameter values to bind the circuit against.\n",
+    "- (Optional) A dictionary of run options, such as the shot count.\n",
+    "\n",
+    "### Sampler outputs\n",
+    "| Name   | Type                                   | Description                                                                               | Example                         |\n",
+    "|--------|----------------------------------------|-------------------------------------------------------------------------------------------|---------------------------------|\n",
+    "| result | `list[ArrayLike[dict[str, int]]]`      | The resultant list of counts dictionaries corresponding to the list of input PUBs.       | `[{'000001': 100, '000011': 2}]` |\n",
+    "\n",
+    "\n",
+    "### Estimator inputs\n",
+    "| Name       | Type                                          | Description                                                                                                 | Required | Example                                                                                                             |\n",
+    "|------------|-----------------------------------------------|-------------------------------------------------------------------------------------------------------------|----------|---------------------------------------------------------------------------------------------------------------------|\n",
+    "| pubs       | `EstimatorPubLike` or `list[EstimatorPubLike]` | One or more tuples containing the inputs listed under `EstimatorPubLike` components.                          | Yes      | `(circuit, observables, parameter_values)`                                                                                  |\n",
+    "| instance   | str                                           | The hub/group/project to use in that format.                                                               | Yes      | `\"hub1/group1/project1\"`                                                                                           |\n",
+    "| run_options| dict                                          | Input options which includes the following: (Optional) `backend_name`: str, `shots`: int. Defaults to least busy backend and 4000 shots. | No       | `{\"backend_name\": backend_name, \"shots\": 2048}`                                                                   |\n",
+    "\n",
+    "**`EstimatorPubLike` components:**\n",
+    "- A single circuit in the OpenQASM 2.0 or 3.0 string format.\n",
+    "- One or more observables that specify the expectation values to estimate, in any of the formats denoted in the list \"Accepted observables formats\".\n",
+    "- (Optional) A collection of parameter values to bind the circuit against.\n",
+    "- (Optional) A dictionary of run options, such as the shot count.\n",
+    "\n",
+    "**Accepted observables formats:**\n",
+    "- A Pauli string: `\"XY\"`\n",
+    "- A dictionary - Pauli strings with coefficients: `{\"XY\": 0.5, \"YZ\": 0.3}`\n",
+    "- A list of Pauli strings: `[\"XY\", \"YZ\", \"ZX\"]`\n",
+    "- A list of Pauli strings with coefficients: `[(\"XY\", 0.5), (\"YZ\", 0.3)]`\n",
+    "- A nested list of Pauli strings: `[[\"XY\", \"YZ\"], [\"ZX\", \"XX\"]]`\n",
+    "- A nested list of Pauli strings with coefficients: `[[(\"XY\", 0.1), (\"YZ\", 0.2)], [(\"ZX\", 0.3), (\"XX\", 0.4)]]`\n",
+    "\n",
+    "### Estimator outputs\n",
+    "| Name   | Type                            | Description                                                                                      | Example             |\n",
+    "|--------|---------------------------------|--------------------------------------------------------------------------------------------------|---------------------|\n",
+    "| values | `valueslist[ArrayLike[float]]`  | The resultant list of expectation values corresponding to the list of input PUBs.               | `[0.43046065915270004]` |\n",
+    "\n",
+    "### Parameter formats\n",
+    "The Sampler and Estimator both accept parameters, which follow the same [array broadcasting rules](./primitive-input-output#broadcasting-rules) as the `QiskitRuntime` primitives. The following parameter formats are accepted:\n",
+    "\n",
+    "| Format                                      | Example                                                                                           |\n",
+    "|---------------------------------------------|---------------------------------------------------------------------------------------------------|\n",
+    "| 0-d array (single binding)                  | `{\"a\": 4, (\"b\", \"c\"): [5, 6]}`                                                                    |\n",
+    "| Single array (last index for parameters)    | `{\"a[0]\", \"a[1]\", \"a[2]\": [0.1, 0.2, 0.3]}`                                                     |\n",
+    "| Multiple arrays (last index for parameters, flexible dimensions) | `{\"c\": np.ones((10, 10, 2)).tolist(), \"b\": np.zeros((10, 10)).tolist()}`                       |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "895db40f",
+   "metadata": {},
+   "source": [
+    "## Benchmarks\n",
+    "\n",
+    "[Published algorithmic benchmarking](https://journals.aps.org/prapplied/abstract/10.1103/PhysRevApplied.20.024034) results demonstrate significant performance improvement across various algorithms, including Bernstein-Vazirani, quantum Fourier transform, Grover’s search, quantum approximate optimization algorithm, and variational quantum eigensolver. The rest of this section provides more details about types of algorithms you can run, as well as the expected performance and runtimes.\n",
+    "\n",
+    "The following independent studies demonstrate how Q-CTRL's Performance Management enables algorithmic research at record-breaking scale:\n",
+    "- [Parametrized Energy-Efficient Quantum Kernels for Network Service Fault Diagnosis](https://arxiv.org/abs/2405.09724v1) - up to 50-qubit quantum kernel learning\n",
+    "- [Tensor-based quantum phase difference estimation for large-scale demonstration](https://arxiv.org/abs/2408.04946) - up to 33-qubit quantum phase estimation\n",
+    "- [Hierarchical Learning for Quantum ML: Novel Training Technique for Large-Scale Variational Quantum Circuits](https://arxiv.org/abs/2311.12929) - up to 21-qubit quantum data loading\n",
+    "\n",
+    "The following table provides a rough guide on accuracy and runtimes from prior benchmarking runs on `ibm_fez`. Performance on other devices may vary. The usage time is based on an assumption of 10,000 shots per circuit. The \"Number of qubits\" indicated is not a hard limitation but represents rough thresholds where you can expect extremely consistent solution accuracy. Larger problem sizes have been successfully solved, and testing beyond these limits is encouraged.\n",
+    "\n",
+    "\n",
+    "| Example    | Number of qubits | Accuracy | Measure of accuracy | Total time (s) | Runtime usage (s) | Primitive (Mode) |\n",
+    "| ---------  | ---------------- | -------------------------- | -------- | ---------- | ------------- |------------- |\n",
+    "| Bernstein–Vazirani  |  50Q    | 100%  | Success Rate (Percentage of runs where the correct answer is the highest count bitstring)     | 10    | 8         | Sampler |\n",
+    "| Quantum Fourier Transform | 30Q              | 100% | Success Rate (Percentage of runs where the correct answer is the highest count bitstring)      | 10    | 8        | Sampler |\n",
+    "| Quantum Phase Estimation  | 30Q   | 99.9998%  | Accuracy of the angle found: `1- abs(real_angle - angle_found)/pi`  | 10  | 8  | Sampler |\n",
+    "| Quantum simulation: Ising model (15 steps) | 20Q  | 99.775%   |  $A$ (defined below)  |  60 (per step)  | 15 (per step)   | Estimator |\n",
+    "| Quantum simulation 2: molecular dynamics (20 time points) | 34Q  |  96.78%  |  $A_{mean}$ (defined below)   | 10 (per time point)    | 6 (per time point)  | Estimator |\n",
+    "\n",
+    "Defining the accuracy of the measurement of an expectation value - the metric $A$ is defined as follows:\n",
+    "$$\n",
+    "A = 1 - \\frac{|\\epsilon^{ideal} - \\epsilon^{meas}|}{\\epsilon^{ideal}_{max} - \\epsilon^{ideal}_{min}},\n",
+    "$$\n",
+    "where $ \\epsilon^{ideal} $ = ideal expectation value,  $ \\epsilon^{meas} $ = measured expectation value, $\\epsilon^{ideal}_{max} $ = ideal maximum value, and $\\epsilon^{ideal}_{min}$ = ideal minimum value. $A_{mean}$ is simply the average of the value of $A$ across multiple measurements.\n",
+    "\n",
+    "This metric is used because it is invariant to global shifts and scaling in the range of attainable values. In other words, regardless of whether you shift the range of possible expectation values higher or lower or increase the spread, the value of $A$ should remain consistent."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73390a19",
+   "metadata": {},
+   "source": [
+    "## Get started\n",
+    "\n",
+    "Authenticate using your [IBM Quantum&trade; Platform API token](http://quantum.ibm.com/), and select the Qiskit Function as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "95a715d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit_ibm_catalog import QiskitFunctionsCatalog\n",
+    "\n",
+    "# Credentials\n",
+    "token = \"<YOUR_IQP_API_TOKEN>\"\n",
+    "hub = \"<YOUR_IQP_HUB>\"\n",
+    "group = \"<YOUR_IQP_GROUP>\"\n",
+    "project = \"<YOUR_IQP_PROJECT>\"\n",
+    "\n",
+    "# Authentication\n",
+    "catalog = QiskitFunctionsCatalog(token=token)\n",
+    "\n",
+    "# Access Function\n",
+    "perf_mgmt = catalog.get(\"q-ctrl/performance_management\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8837f5f",
+   "metadata": {},
+   "source": [
+    "## Example: Sampler\n",
+    "\n",
+    "Use the Sampler mode of Fire Opal Performance Management to run a Bernstein–Vazirani circuit. This algorithm, used to find a hidden string from the outputs of a black box function, is a common benchmarking algorithm because there is a single correct answer."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39819cbd",
+   "metadata": {},
+   "source": [
+    "### 1. Create the circuit\n",
+    "Define the correct answer to the algorithm, the hidden bitstring, and the Bernstein–Vazirani circuit. You can adjust the width of the circuit by simply changing the `circuit_width`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "278c5298",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import qiskit\n",
+    "\n",
+    "circuit_width = 35\n",
+    "hidden_bitstring = \"1\" * circuit_width\n",
+    "\n",
+    "# Create circuit, reserving one qubit for BV oracle\n",
+    "bv_circuit = qiskit.QuantumCircuit(circuit_width + 1, circuit_width)\n",
+    "bv_circuit.x(circuit_width)\n",
+    "bv_circuit.h(range(circuit_width + 1))\n",
+    "for input_qubit, bit in enumerate(reversed(hidden_bitstring)):\n",
+    "    if bit == \"1\":\n",
+    "        bv_circuit.cx(input_qubit, circuit_width)\n",
+    "bv_circuit.barrier()\n",
+    "bv_circuit.h(range(circuit_width + 1))\n",
+    "bv_circuit.barrier()\n",
+    "for input_qubit in range(circuit_width):\n",
+    "    bv_circuit.measure(input_qubit, input_qubit)\n",
+    "\n",
+    "# Create PUB tuple\n",
+    "pubs = [(bv_circuit,)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "86d47228",
+   "metadata": {},
+   "source": [
+    "### 2. Run the circuit\n",
+    "Run the circuit and optionally define the backend and number of shots."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2ad187e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Choose a backend or remove this option to default to the least busy device\n",
+    "backend_name = \"<CHOOSE_A_BACKEND>\"\n",
+    "\n",
+    "# Run the circuit using the sampler\n",
+    "qctrl_sampler_job = perf_mgmt.run(\n",
+    "    runner_function =\"sampler\",\n",
+    "    pubs = pubs,\n",
+    "    instance = hub + \"/\" + group + \"/\" + project,\n",
+    "    run_options = {\"backend_name\": backend_name},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6bfaf53",
+   "metadata": {},
+   "source": [
+    "You can use the familiar [Qiskit Serverless APIs](./serverless) to check your Qiskit Function workload's status:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e408b89e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(qctrl_sampler_job.status())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a371424",
+   "metadata": {},
+   "source": [
+    "### 3. Retrieve the result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b4ac097c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Retrieve the counts from the result list\n",
+    "counts = qctrl_sampler_job.result()[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8ba9fd35",
+   "metadata": {},
+   "source": [
+    "In case you need to retrieve results later, you can run the following cell to save your job ID and use it to retrieve your job."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bc4df554",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qctrl_sampler_job_id = qctrl_sampler_job.job_id\n",
+    "print(qctrl_sampler_job_id)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71a7e415",
+   "metadata": {},
+   "source": [
+    "You can use the job ID to retrieve your job object, which can be used to get the status or results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6951d866",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qctrl_sampler_job = catalog.get_job_by_id(qctrl_sampler_job_id)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df2788d5",
+   "metadata": {},
+   "source": [
+    "## Example: Estimator\n",
+    "\n",
+    "Use the Sampler mode of Fire Opal Performance Management to run a Bernstein–Vazirani circuit. This algorithm, used to find a hidden string from the outputs of a black box function, is a common benchmarking algorithm because there is a single correct answer."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "82932760",
+   "metadata": {},
+   "source": [
+    "In addition to the `qiskit-ibm-catalog` and `qiskit` package, you will also use the `NumPy` package to run this example. You can install this package by uncommenting the following cell if you are running this example in a notebook using the IPython kernel."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "70bb93ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %pip install numpy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02b2a79a",
+   "metadata": {},
+   "source": [
+    "### 1. Create the circuit\n",
+    "As an example, generate a random Hermitian operator and an observable to input to the Performance Management function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "32037218",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from qiskit.circuit.library import IQP\n",
+    "from qiskit.quantum_info import random_hermitian\n",
+    "\n",
+    "n_qubits = 50\n",
+    "\n",
+    "# Generate a random circuit\n",
+    "mat = np.real(random_hermitian(n_qubits, seed=1234))\n",
+    "circuit = IQP(mat)\n",
+    "circuit.measure_all()\n",
+    "\n",
+    "# Define observables as a string\n",
+    "observable = \"Z\" * n_qubits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "476fae6a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create PUB tuple\n",
+    "pubs = [(circuit, observable)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "09e64cd7",
+   "metadata": {},
+   "source": [
+    "### 2. Run the circuit\n",
+    "Run the circuit and optionally define the backend and number of shots."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "720c6919",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Choose a backend or remove this option to default to the least busy device\n",
+    "backend_name = \"<CHOOSE_A_BACKEND>\"\n",
+    "\n",
+    "qctrl_estimator_job = perf_mgmt.run(\n",
+    "    runner_function = \"estimator\",\n",
+    "    pubs = pubs,\n",
+    "    instance = hub + \"/\" + group + \"/\" + project,\n",
+    "    run_options = {\"backend_name\": backend_name},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c07af9d0",
+   "metadata": {},
+   "source": [
+    "You can use the familiar [Qiskit Serverless APIs](./serverless) to check your Qiskit Function workload's status:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7e2fab85",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(qctrl_estimator_job.status())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23556c8e",
+   "metadata": {},
+   "source": [
+    "### 3. Retrieve the result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8be67088",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Retrieve the counts from the result list\n",
+    "expectation_value = qctrl_estimator_job.result()[0]\n",
+    "\n",
+    "print(f\"Expectation value: {expectation_value}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a56975a",
+   "metadata": {},
+   "source": [
+    "In case you need to retrieve results later, you can run the following cell to save your job ID."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0a5baa05",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qctrl_estimator_job_id = qctrl_estimator_job.job_id\n",
+    "print(qctrl_estimator_job_id)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "438469d9",
+   "metadata": {},
+   "source": [
+    "You can use the job ID to retrieve your job object, which can be used to get the status or results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0324580e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qctrl_estimator_job = catalog.get_job_by_id(qctrl_estimator_job_id)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bb33258d",
+   "metadata": {},
+   "source": [
+    "## Get support\n",
+    "\n",
+    "For any questions or issues, [contact Q-CTRL](https://form.typeform.com/to/iuujEAEI)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2fe4a6ea",
+   "metadata": {},
+   "source": [
+    "## Next steps\n",
+    "\n",
+    "<Admonition type=\"tip\" title=\"Recommendations\">\n",
+    "\n",
+    "- Request access to [Q-CTRL Performance Management](https://quantum.ibm.com/functions?id=c750648c-ba44-4137-8c34-4140a3aaa7a9)\n",
+    "- Try the [Q-CTRL Performance Management on variational quantum algorithms](https://learning.quantum.ibm.com/tutorial/q-ctrl-performance-management-on-variational-quantum-algorithms) tutorial.\n",
+    "\n",
+    "</Admonition>"
+   ]
+  }
+ ],
+ "metadata": {
+  "description": "Apply automated error suppression using Fire Opal Performance Management by Q-CTRL",
+  "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"
+  },
+  "title": "Performance Management - A Qiskit Function by Q-CTRL Fire Opal"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/guides/qedma-qesem.ipynb b/docs/guides/qedma-qesem.ipynb
new file mode 100644
index 0000000000..ca7b99767e
--- /dev/null
+++ b/docs/guides/qedma-qesem.ipynb
@@ -0,0 +1,344 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "dde95705",
+   "metadata": {},
+   "source": [
+    "# QESEM: A Qiskit Function by Qedma\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "While quantum processing units have vastly improved in recent years, errors due to noise and imperfections in existing hardware remain a central challenge for quantum algorithm developers. As the field approaches utility-scale quantum computations that cannot be verified classically, solutions for canceling noise with guaranteed accuracy are becoming increasingly important. To overcome this challenge, Qedma has developed Quantum Error Suppression and Error Mitigation (QESEM), seamlessly integrated on IBM Quantum&trade; Platform as a [Qiskit Function](/guides/functions).\n",
+    "\n",
+    "With QESEM, users can run their quantum circuits on noisy QPUs to obtain highly accurate error-free results with highly efficient QPU-time overheads, close to fundamental bounds. To achieve this, QESEM leverages a suite of propriety methods developed by Qedma, for the characterization and reduction of errors. Error reduction techniques include gate optimization, noise-aware transpilation, error suppression (ES), and unbiased error mitigation (EM). With this combination of these characterization-based methods, users can achieve reliable, error-free results for generic large-volume quantum circuits, unlocking applications that cannot be accomplished otherwise."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f761442",
+   "metadata": {},
+   "source": [
+    "## Description\n",
+    "\n",
+    "You can use the QESEM function by Qedma to easily estimate and execute your circuits with error suppression and mitigation, achieving larger circuit volumes and higher accuracies. To use QESEM, you provide a quantum circuit, a set of observables to measure, a target statistical accuracy for each observable, and a chosen QPU. Before you run the circuit to the target accuracy, you can estimate the required QPU time based on an analytical calculation that does not require circuit execution. Once you are satisfied with the QPU time estimation, you can execute the circuit with QESEM.\n",
+    "\n",
+    "When you execute a circuit, QESEM runs a device characterization protocol tailored to your circuit, yielding a reliable noise model for the errors occurring in the circuit. Based on the characterization, QESEM first implements noise-aware transpilation to map the input circuit onto a set of physical qubits and gates, which minimizes the noise affecting the target observable. These include the natively available gates (CX/CZ on IBM&reg; devices), as well as additional gates optimized by QESEM, forming QESEM’s extended gate set. QESEM then runs a set of characterization-based ES and EM circuits on the QPU and collects their measurement outcomes. These are then classically post-processed to provide an unbiased expectation value and an error bar for each observable, corresponding to the requested accuracy.\n",
+    "\n",
+    "![Qedma QESEM overview](/images/guides/qedma-qesem/overview.svg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "74823696",
+   "metadata": {},
+   "source": [
+    "## Unique error mitigation features\n",
+    "QESEM has been demonstrated to provide high-accuracy results for a variety of quantum applications and on the largest circuit volumes achievable today. QESEM offers the following user-facing features, demonstrated in the benchmarks section below:\n",
+    "1.\t**Guaranteed accuracy:** QESEM outputs unbiased estimations for expectation values of observables. Its EM method is equipped with theoretical guarantees, which - together with Qedma’s cutting-edge characterization - ensure the mitigation converges to the noiseless circuit output up to the user-specified accuracy. In contrast to many heuristic EM methods that are prone to systematic errors or biases, QESEM’s guaranteed accuracy is essential for ensuring reliable results in generic quantum circuits and observables.\n",
+    "2.\t**Scalability to large QPUs:** QESEM’s QPU time depends on circuit volumes, but is otherwise independent of the number of qubits. Qedma has demonstrated QESEM on the largest quantum devices available today, including the IBM 127-qubit Eagle and 133-qubit Heron devices.\n",
+    "3.\t**Application-agnostic:** QESEM has been demonstrated on a variety of applications, including Hamiltonian simulation, VQE, QAOA, and amplitude estimation. Users can input any quantum circuit and observable to be measured, and obtain accurate error-free results. The only limitations are dictated by the hardware specifications and allocated QPU time, which determine the accessible circuit volumes and output accuracies. In contrast, many error reduction solutions are application-specific or involve uncontrolled heuristics, rendering them inapplicable for generic quantum circuits and applications.\n",
+    "4.  **Extended gate set:** QESEM supports fractional-angle gates, and provides Qedma-optimized fractional-angle $Rzz(\\theta)$ gates on IBM Eagle devices. This extended gate set enables more efficient compilation and unlocks circuit volumes larger by a factor of up to 2 compared to default CX/CZ compilation.\n",
+    "5.\t**Multibase observables:** QESEM supports input observables composed of many non-commuting Pauli strings, such as generic Hamiltonians. The choice of measurement bases and the optimization of QPU resource allocation (shots and circuits) is then performed automatically by QESEM to minimize the required QPU time for the requested accuracy. This optimization, which takes into account hardware fidelities and execution rates, enables you to run deeper circuits and obtain higher accuracies."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b901258",
+   "metadata": {},
+   "source": [
+    "## Function parameters\n",
+    "\n",
+    "| Name |  Type | Description | Required | Default |  Example |\n",
+    "| -----| ------| ------------| -------- | ------- | -------- |\n",
+    "| `instance` | str|  The hub/group/project to use in that format |  No |  N/A | “hub1/group1/project1”  |\n",
+    "| `action` | str | The required action: \"estimate_qpu_time\" or \"execute\" |  Yes  | N/A | \"execute\" |\n",
+    "| `pubs` | Iterable[EstimatorPubLike] | A pub-like object in the form of (circuit, observables) |  Yes|  N/A |  (circ1, [obs1,obs2,obs3])  |\n",
+    "| `precision` | float | The target precision for expectation value estimates of each observable |  Yes|  N/A |  0.03  |\n",
+    "| `run_options` | dict | Includes the name of the backend to run on | Yes | N/A  |  `{ backend_name = \"ibm_fez\" }`  |\n",
+    "| `custom_options` |dict |  Advanced features: `transpilation_level` and `max_qpu_time` (in seconds)|  No  | `max_qpu_time` = 14400 , `transpilation_level`= 1  | `{ max_qpu_time = 14400, transpilation_level = 0 }` |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73390a19",
+   "metadata": {},
+   "source": [
+    "## Get started\n",
+    "\n",
+    "Authenticate using your [IBM Quantum&trade; Platform API token](http://quantum.ibm.com/), and select the Qiskit Function as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "95a715d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit_ibm_catalog import QiskitFunctionsCatalog\n",
+    "\n",
+    "catalog = QiskitFunctionsCatalog(instance=\"\")\n",
+    "\n",
+    "qesem  = catalog.load('qedma/qesem')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8837f5f",
+   "metadata": {},
+   "source": [
+    "## Example\n",
+    "\n",
+    "The QESEM function must get the following parameters to be executed:\n",
+    "- action\n",
+    "- pubs (circuit, observables)\n",
+    "- precision\n",
+    "- run_options\n",
+    "\n",
+    "\n",
+    "To get started, try the basic example of estimating QPU time:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d56e1440",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "job = qesem_function.run(\n",
+    "    action = \"estimate_qpu_time\",\n",
+    "    instance = \"hub/group/project\",\n",
+    "    pubs = [\n",
+    "        (\n",
+    "            bell,\n",
+    "            [obs1,obs2,obs3]\n",
+    "        )\n",
+    "    ],\n",
+    "    precision = 0.03,\n",
+    "    run_options = {\n",
+    "        backend_name = \"ibm_brisbane\",\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e5869f3",
+   "metadata": {},
+   "source": [
+    "The following example executes a QESEM job:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e7351d6b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# example execute QESEM job\n",
+    "job = qesem_function.run(\n",
+    "    action = \"execute\",\n",
+    "    instance = \"hub/group/project\",\n",
+    "    pubs = [\n",
+    "        (\n",
+    "            bell,\n",
+    "            [obs1,obs2,obs3]\n",
+    "        )\n",
+    "    ],\n",
+    "    precision = 0.03,\n",
+    "    run_options = {\n",
+    "        backend_name = \"ibm_brisbane\",\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "399b0deb",
+   "metadata": {},
+   "source": [
+    "<Admonition type=\"note\">\n",
+    "- The `precision` parameter signifies the acceptable error on the expectation values of the observables in absolute value.  Namely, the QPU runtime for mitigation will be determined to provide output values for all the observables of interest that fall within a `1σ` confidence interval of the target precision. If multiple observables are provided, the mitigation will run until the target precision is reached for each of the input observables.\n",
+    "\n",
+    "- Currently QESEM supports a single `PUB`.\n",
+    "</Admonition>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03998691",
+   "metadata": {},
+   "source": [
+    "You can use the familiar Qiskit Serverless APIs to check your Qiskit Function workload's status or return results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "856fe992",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(job.status())\n",
+    "result = job.result()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e2817b13",
+   "metadata": {},
+   "source": [
+    "## Custom options\n",
+    "\n",
+    "Provide the `custom_options` parameter to set additional advanced features for the QESEM function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c2ce65e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# example execute QESEM job\n",
+    "job = qesem_function.run(\n",
+    "    action = \"execute\",\n",
+    "    instance = \"hub/group/project\",\n",
+    "    pubs = [\n",
+    "        (\n",
+    "            bell,\n",
+    "            [obs1,obs2,obs3]\n",
+    "        )\n",
+    "    ],\n",
+    "    precision = 0.03,\n",
+    "    run_options = {\n",
+    "        backend_name = \"ibm_brisbane\",\n",
+    "    },\n",
+    "    custom_options = {\n",
+    "        max_qpu_time = 14400,\n",
+    "        transpilation_level = 0\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bcd50b21",
+   "metadata": {},
+   "source": [
+    "`max_qpu_time`: Allows you to limit the QPU time to be used for the entire QESEM process. Since the final QPU time required to reach the target accuracy is determined dynamically during the QESEM job, this parameter enables you to limit the cost of the experiment. If the dynamically-determined QPU time is shorter than the time allocated by the user, this parameter will not affect the experiment. The `max_qpu_time` parameter is particularly useful in cases where the analytical time estimate provided by QESEM before the job starts is too pessimistic and the user wants to initiate a mitigation job anyway. After the time limit it reached, QESEM stops sending new circuits. Circuits that have already been sent continue running (so the total time may surpass the limit by up to 30 minutes), and the user receives the processed results from the circuits that ran up to that point. If you want to apply a QPU time limit shorter than the analytical time estimate, consult with Qedma to obtain an estimate for the accuracy achievable within the time limit.\n",
+    "\n",
+    "\n",
+    "`transpilation_level`: After a circuit is submitted to QESEM, it automatically prepares several alternative circuit transpilations and chooses the one that minimizes QPU time. For instance, alternative transpilations might utilize Qedma-optimized fractional RZZ gates to reduce the circuit depth. Of course, all transpilations are equivalent to the input circuit, in terms of their ideal output.\n",
+    "\n",
+    "To exert more control over the circuit transpilation, set the transpilation level in the `QesemOptions`. While ‘level 1’ corresponds to the default behavior described above, ‘level 0’ includes only minimal modifications required for high-accuracy output; for example, ‘layerification’ - the organization of circuit operations into ‘layers’ of simultaneous two-qubit gates. Note that automatic hardware-mapping onto high-fidelity qubits is applied in any case.\n",
+    "\n",
+    "| `transpilation_level` | description |\n",
+    "|:-:|:--|\n",
+    "| `1` | Default QESEM transpilation. Prepares several alternative transpilations and chooses the one that minimizes QPU time. Barriers may be modified in the layerification step. |\n",
+    "| `0` | Minimal transpilation: the mitigated circuit will closely resemble the input circuit structurally. Circuits provided in level 0 should match the device connectivity and should be specified in terms of the following gates: CX, Rzz(α), and standard single-qubit gates (U, x, sx, rz, etc). Barriers will be respected in the layerification step. |\n",
+    "\n",
+    "<Admonition type=\"note\">\n",
+    "Qiskit barriers are typically used to specify the layers of two-qubit gates in quantum circuits. In level 0, QESEM preserves the layers specified by the barriers. In level 1, the layers specified by the barriers are considered as one transpilation alternative when minimizing QPU time.\n",
+    "</Admonition>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "895db40f",
+   "metadata": {},
+   "source": [
+    "## Benchmarks\n",
+    "\n",
+    "QESEM has been tested on a wide variety of use cases and applications. The following examples can assist you with assessing which types of workloads you can run with QESEM.\n",
+    "\n",
+    "A key figure of merit for quantifying the hardness of both error mitigation and classical simulation for a given circuit and observable is **active volume**: the number of CNOT gates affecting the observable in the circuit. The active volume depends on the circuit depth and width, on the observable weight, and on the circuit structure, which determines the lightcone of the observable. For further details, see the talk from the [2024 IBM Quantum Summit](https://www.youtube.com/watch?v=Hd-IGvuARfE&t=1730s). QESEM provides particularly large value in the high-volume regime, giving reliable results for generic circuits and observables.\n",
+    "\n",
+    "![Active volume](/images/guides/qedma-qesem/active_volume.svg)\n",
+    "\n",
+    "\n",
+    "| Application    | Number of qubits | Device | Circuit description | Accuracy | Total time | Runtime usage |\n",
+    "| ---------  | ---------------- | ----- | -------------------------- | -------- | ---------- | ------------- |\n",
+    "| VQE circuit  | 8              | Eagle (r3) | 21 total layers, 9 measurement bases, 1D chain                    | 98%      | 35 min      | 14 min         |\n",
+    "| Kicked Ising   | 28              | Eagle (r3) | 3 unique layers x 3 steps, 2D heavy-hex topology                      | 97%     | 22 min      | 4 min          |\n",
+    "| Kicked Ising   | 28              | Eagle (r3) | 3 unique layers x 8 steps, 2D heavy-hex topology                     | 97%      | 116 min      | 23 min          |\n",
+    "| Trotterized Hamiltonian simulation   | 40  | Eagle (r3)            | 2 unique layers x 10 Trotter steps, 1D chain                    | 97%      | 3 hours     | 25 min         |\n",
+    "| Trotterized Hamiltonian simulation   | 119  | Eagle (r3)           | 3 unique layers x 9 Trotter steps, 2D heavy-hex topology                    | 95%      | 6.5 hours     | 45 min         |\n",
+    "| Kicked Ising  | 136             | Heron (r2) | 3 unique layers x 15 steps, 2D heavy-hex topology                    | 99%      | 52 min             | 9 min           |\n",
+    "\n",
+    "Accuracy is measured here relative to the ideal value of the observable: $\\frac{\\langle O \\rangle_{ideal} - \\epsilon}{\\langle O \\rangle_{ideal}}$, where '$\\epsilon$' is the absolute precision of the mitigation (set by the user input), and $\\langle O \\rangle_{ideal}$ is the observable at the noiseless circuit.\n",
+    "'Runtime usage' measures the usage of the benchmark in batch mode (sum over usage of individual jobs), whereas 'total time' measures usage in session mode (experiment wall time), which includes additional classical and communication times. QESEM is available for execution in both modes, so that users can make the best use of their available resources.\n",
+    "\n",
+    "The 28-qubit Kicked Ising circuits simulate the Discrete Time Quasicrystal studied by Shinjo et al. (see [arXiv 2403.16718](https://arxiv.org/abs/2403.16718) and [Q2B24 Tokyo](https://www.youtube.com/watch?v=tQW6FdLc6zo)) on three connected loops of ibm_kawasaki. The circuit parameters taken here are $(\\theta_x, \\theta_z) = (0.9 \\pi, 0)$, with a ferromagnetic initial state $| \\psi_0 \\rangle = | 0 \\rangle ^{\\otimes n}$. The measured observable is the absolute value of the magnetization $M = |\\frac{1}{28} \\sum_{i=0}^{27} \\langle Z_i \\rangle|$. The utility-scale Kicked Ising experiment was run on the 136 best qubits of ibm_fez; this particular benchmark was run at the Clifford angle $(\\theta_x, \\theta_z) = (\\pi, 0)$, at which the active volume grows slowly with circuit depth, which - together with the high device fidelities - enables high accuracy at a short runtime.\n",
+    "\n",
+    "Trotterized Hamiltonian simulation circuits are for a Transverse-Field Ising model at fractional angles: $(\\theta_{zz}, \\theta_x) = (\\pi / 4, \\pi /8)$ and $(\\theta_{zz}, \\theta_x) = (\\pi / 6, \\pi / 8)$ correspondingly (see [Q2B24 Tokyo](https://www.youtube.com/watch?v=tQW6FdLc6zo)). The utility-scale circuit was run on the 119 best qubits of ibm_brisbane, whereas the 40-qubit experiment was run on the best available chain. The accuracy is reported for the magnetization; high-accuracy results were obtained for higher-weight observables as well.\n",
+    "\n",
+    "The VQE circuit was developed together with researchers from the Center for Quantum Technology and Applications at the Deutsches Elektronen-Synchrotron (DESY). The target observable here was a Hamiltonian consisting of a large number of non-commuting Pauli strings, emphasizing QESEM's optimized performance for multi-basis observables. Mitigation was applied to a classically-optimized ansatz; although these results are still unpublished, results of the same quality will be obtained for different circuits with similar structural properties."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e9ec2e67",
+   "metadata": {},
+   "source": [
+    "## Get support\n",
+    "\n",
+    "The Qedma support team is here to help! If you encounter any issues or have questions about using the QESEM Qiskit Function, please don't hesitate to reach out. Our knowledgeable and friendly support staff are ready to assist you with any technical concerns or inquiries you may have.\n",
+    "\n",
+    "You can email us at support@qedma.com for assistance. Please include as much detail as possible about the issue you're experiencing to help us provide a swift and accurate response. You can also contact your dedicated Qedma POC representative via email or phone.\n",
+    "\n",
+    "To help us assist you more efficiently, please provide the following information when you contact us:\n",
+    "\n",
+    "- A detailed description of the issue\n",
+    "- Any relevant error messages or codes\n",
+    "- Steps to reproduce the issue\n",
+    "- Your contact information\n",
+    "\n",
+    "\n",
+    "We are committed to providing you with prompt and effective support to ensure you have the best possible experience with our API.\n",
+    "\n",
+    "We are always looking to improve our product and we welcome your suggestions! If you have ideas on how we can enhance our services or features you'd like to see, please send us your thoughts to support@qedma.com"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a6a25c8",
+   "metadata": {},
+   "source": [
+    "## Next steps\n",
+    "\n",
+    "<Admonition type=\"tip\" title=\"Recommendations\">\n",
+    "\n",
+    "- [Request access to Qedma QESEM](https://quantum.ibm.com/functions?id=7636f111-b7b8-4a00-b22d-5fcc5c5cb3d0)\n",
+    "\n",
+    "</Admonition>"
+   ]
+  }
+ ],
+ "metadata": {
+  "description": "Run quantum circuits on noisy QPUs to obtain highly accurate error-free results with highly efficient QPU-time overheads, close to fundamental bounds.",
+  "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"
+  },
+  "title": "QESEM - A Qiskit Function by Qedma"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/guides/qunasys-quri-chemistry.ipynb b/docs/guides/qunasys-quri-chemistry.ipynb
new file mode 100644
index 0000000000..cd3a98bad0
--- /dev/null
+++ b/docs/guides/qunasys-quri-chemistry.ipynb
@@ -0,0 +1,886 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e9bd392e-b6a0-4ff2-a5fd-09f204311a52",
+   "metadata": {},
+   "source": [
+    "# QURI Chemistry: a Qiskit Function by QunaSys\n",
+    "\n",
+    "{/* cspell:ignore QSCI, CCSD, UCCSD, imags, CCGSD, CISD */}\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "This function helps you solve the quantum chemistry ground state estimation problem by using one of two algorithms based on the [Quantum Selected Configuration Interaction (QSCI) algorithm](https://arxiv.org/pdf/2302.11320):\n",
+    "\n",
+    "- QSCI\n",
+    "- Optimization-based QSCI\n",
+    "\n",
+    "The overall procedure for using this functions is summarized in the flow chart below:\n",
+    "\n",
+    "![Workflow](/images/guides/qunasys-qsci/workflow.svg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3194374e-bef1-4778-bd74-91629bbc7774",
+   "metadata": {},
+   "source": [
+    "## Function description\n",
+    "\n",
+    "The QSCI algorithm samples from a specific ansatz on quantum computers. Each sampled bitstring corresponds to an electron configuration, and the number of time a bitstring is sampled represents the importance of that electron configuration. Choose a number, $R$, to select the $R$ most important electron configurations from the sample. The function constructs and diagonalizes the subspace Hamiltonian, depending on your choice of $R$. The smallest eigenvalue is termed \"QSCI energy\", which is the estimation of the QSCI algorithm's true ground state energy.\n",
+    "\n",
+    "One important QSCI ingredient is its initial state preparations. Several possible initial state preparations are provided in this function. These include the [hardware efficient](https://www.nature.com/articles/nature23879), [UCCSD](https://www.nature.com/articles/srep03589), [kUpCCGSD](https://arxiv.org/abs/1810.02327), and [kuCJ](https://arxiv.org/abs/1909.12410) ansatzes. A CCSD amplitude-based double excitation ansatz is provided specifically to be executed on near-term devices. Ansatz-specific settings are also provided for you to customize according to your needs.\n",
+    "\n",
+    "In addition to returning the QSCI energy, the function also computes the estimated ground state wave function to help you identify the important electron configurations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "76762731-4a8f-4b95-b26a-ed8245f728df",
+   "metadata": {},
+   "source": [
+    "## Get started\n",
+    "\n",
+    "First, authenticate using your [IBM Quantum&trade; API token](http://quantum.ibm.com/), and select the Qiskit Function as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "af1ca24a-94cd-48a0-b213-e79de5e32116",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit_ibm_runtime import QiskitRuntimeService\n",
+    "from qiskit_ibm_catalog import QiskitFunctionsCatalog\n",
+    "\n",
+    "token = QiskitRuntimeService.saved_accounts()[\"default-ibm-quantum\"][\"token\"]\n",
+    "catalog = QiskitFunctionsCatalog(token=token)\n",
+    "\n",
+    "function = catalog.load(\"qunasys/quri-chemistry\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2738b51-57cf-4559-bf33-5d1ccc8104f9",
+   "metadata": {},
+   "source": [
+    "Call the function with the following arguments:\n",
+    "\n",
+    "```python\n",
+    "function.run(\n",
+    "    method = ...,       # Allow only \"QSCI\" or \"OPT_QSCI\"\n",
+    "    parameters = {...}, # A JSON dictionary representing all the algorithm setting\n",
+    "    instance = ...,     # Your instance string.\n",
+    "    backend = ...       # The backend name you want to use.\n",
+    ")\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "940e4a14-bebe-4b5a-8f00-208cc0e2c0a6",
+   "metadata": {},
+   "source": [
+    "A detailed description of each argument is summarized in the following table.\n",
+    "\n",
+    "| Name       | Type | Description                        | Required | Default                             | Example           |\n",
+    "| :--------- | :--- | :--------------------------------- | :------- | :---------------------------------- | :---------------- |\n",
+    "| method     | str  | The algorithm name                 | Yes      | -                                   | “QSCI”            |\n",
+    "| parameters | json | Details settings of the algorithm | Yes      | -                                   |                   |\n",
+    "| instance    | str  | Your instance name                 | No       | Your default instance saved on disk | “ibm-q/open/main” |\n",
+    "| backend    | str  | The backend name you want to use   | No       | The least busy one available        | “ibm_torino”      |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c885e70d-9d32-492e-a5ea-5eb987547f99",
+   "metadata": {},
+   "source": [
+    "## Set up the molecule\n",
+    "\n",
+    "Here, you will learn how to build a molecule. The input is exactly the same as the `pyscf.gto.M` with an additional `active_space` option. The following summarizes the detailed settings for configuring a molecule.\n",
+    "\n",
+    "| Name         | Type  | Description                                                                                                                                                      | Required | Default  | Example                                                 |\n",
+    "| :----------- | :---- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------- | :------- | :------- | :------------------------------------------------------ |\n",
+    "| atom         | str   | The list of atom coordinates                                                                                                                                     | Yes      | -        | “H 0 0 0; H 0 0 1”                                      |\n",
+    "| basis        | str   | The basis set to represent the electronic wave function. Allowed values are the same those supported by [PySCF](https://pyscf.org/_modules/pyscf/gto/basis.html) | No       | “sto-3g” | “sto-3g”                                                |\n",
+    "| spin         | float | The $s_z$ quantum number of your molecule. Only spin=0.0 is supported                                                                                            | No       | 0.0      | 0.0                                                     |\n",
+    "| charge       | int   | The total charge of your molecule                                                                                                                                | No       | 0        | 0                                                       |\n",
+    "| active_space | json  | The active space you want to choose. See the “active space” table that follows for more information                                                                     | No       | None     | See the “active space” table that follows for more information |\n",
+    "\n",
+    "\n",
+    "The active space settings are summarized below.\n",
+    "\n",
+    "| Name                | Type      | Description                                | Required | Default | Example   |\n",
+    "| :------------------ | :-------- | :----------------------------------------- | :------- | :------ | :-------- |\n",
+    "| n_active_ele        | int       | The number of active electrons             | Yes      | -       | 4         |\n",
+    "| n_active_orb        | int       | The number of active spatial orbitals      | Yes      | -       | 3         |\n",
+    "| active_orbs_indices | list[int] | The list of active spatial orbital indices | No       | None    | [0, 1, 2] |\n",
+    "\n",
+    "\n",
+    "As an example, you can configure the setting for a $H_2O$ molecule in JSON format:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "7f910acf-1e6f-4e67-b17d-c3b06de7a562",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mole = {\n",
+    "    \"atom\": \"O 0 0 0; H 0.2774 0.8929 0.2544; H 0.6068 -0.2383 -0.7169\",\n",
+    "    \"basis\": \"6-31g\",   # default to \"sto-3g\"\n",
+    "    \"spin\": 0.0,        # default to 0.0. Current code forces spin = 0.0\n",
+    "    \"charge\": 0,        # default to 0\n",
+    "    \"active_space\": {   # default to None. Equivalent to no active space chosen.\n",
+    "        \"n_active_ele\": 10,\n",
+    "        \"n_active_orb\": 10,\n",
+    "        \"active_orbs_indices\": [i for i in range(10)]\n",
+    "    }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f3b38b2-3e22-484f-b8cd-81fd4f5c8031",
+   "metadata": {},
+   "source": [
+    "## Execute algorithms\n",
+    "\n",
+    "All the function input is in JSON format. You will set up a JSON string that specifies the options for running an algorithm. As stated previously, the two types of algorithms provided are \"QSCI\" and \"OPT_QSCI\"."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f39f284-ec75-4def-86d9-60e2c5919322",
+   "metadata": {},
+   "source": [
+    "### QURI Chemistry\n",
+    "\n",
+    "In QURI Chemistry, you need to specify an ansatz, its settings, the state preparation method, and the QSCI setting. Typical QSCI input example:\n",
+    "\n",
+    "```\n",
+    "algo_input = {\n",
+    "    \"mole\": mole,\n",
+    "    \"ansatz\": ...,\n",
+    "    \"state_prep_method\": ...,\n",
+    "    \"ansatz_setting\": ...,\n",
+    "    \"qsci_setting\": {\"n_shots\": ..., \"number_of_states_pick_out\": ...},\n",
+    "    \"mitigation_setting\": {}\n",
+    "},\n",
+    "\n",
+    "```\n",
+    "\n",
+    "| Name               | Type           | Description                                                                                 | Required | Default | Example                                                         |\n",
+    "| :----------------- | :------------- | :------------------------------------------------------------------------------------------ | :------- | :------ | :-------------------------------------------------------------- |\n",
+    "| mole               | json           | The molecule setting. See the table for setting molecules above                             | Yes      |         | The molecule setting. See the table for setting molecules above |\n",
+    "| ansatz             | str            | The name of the ansatz you want to use. Only “DoubleExcitation” and “UCCSD” are supported. `DoubleExcitation` is highly recommended to obtain a stable result, especially for systems larger than eight qubits   | Yes      |         | “DoubleExcitation”                                              |\n",
+    "| state_prep_method  | str            | The way you want to prepare the circuit parameter of your ansatz. Only CCSD is allowed | No       | “CCSD\"  | “CCSD”                                                          |\n",
+    "| ansatz_setting     | str            | Specific setting you want to configure your ansatz with                                     | No       | None    | See the following examples                                          |\n",
+    "| qsci_setting       | dict[str, int] | Specify the number of shots and the size of the subspace. See the following section for details                                                       | Yes      |         | See the QSCI setting table                                      |\n",
+    "| mitigation_setting | json           | Settings for configuring error mitigation                                                   | No       | None    | See the mitigation section below                                |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06d15114-0779-42b9-8239-a7049a9cfbf1",
+   "metadata": {},
+   "source": [
+    "#### QSCI algorithm parameter\n",
+    "\n",
+    "The parameters for setting the QSCI algorithm is summarized in this table.\n",
+    "\n",
+    "| Name                      | Type | Description                                                                                                                                                                          | Required | Example |\n",
+    "| :------------------------ | :--- | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :------- | :------ |\n",
+    "| n_shots                   | int  | The total number of shots to sample from the ansatz                                                                                                                                  | Yes      | 100000  |\n",
+    "| number_of_states_pick_out | int  | Represents the size of the subspace Hamiltonian $R$. It affects the _classical_ resource, where _classical_ diagonalization of the $R \\times R$ subspace Hamiltonian is performed if the number of bitstrings ($n$) sampled from the ansatz is larger than $R$. Otherwise, the $n \\times n$ subspace matrix is diagonalized | Yes      | 10000   |\n",
+    "\n",
+    "This example demonstrates the following setting:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ab4668b8-782a-416c-a5f4-9cebf7dc8808",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qsci_setting = {\"n_shots\": 1e5, \"number_of_states_pick_out\": 1e4}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e963d17c-e6e1-461d-a89d-7f4256643c9d",
+   "metadata": {},
+   "source": [
+    "#### QURI Chemistry with UCCSD\n",
+    "\n",
+    "UCCSD is a common chemistry-inspired ansatz. In the QURI Chemistry function, you can customize it with the `ansatz_setting` key. Options for customizing UCCSD is summarized in the table.\n",
+    "\n",
+    "| Name             | Type | Description                                                                                | Required | Default | Example |\n",
+    "| :--------------- | :--- | :----------------------------------------------------------------------------------------- | :------- | :------ | :------ |\n",
+    "| use_singles      | bool | If True, all the single excitation modes are included. Otherwise, they are all turned off | No       | True    | True    |\n",
+    "| n_trotter        | int  | The number of Trotter steps for the exponentiated excitation operator                     | No       | 1       | 1       |\n",
+    "| reduce_parameter | bool | Only True is allowed for QSCI. refer to the `singlet_excitation` field in the [QURI Parts documentation](https://quri-parts.qunasys.com/api/quri_parts/openfermion/quri_parts.openfermion.ansatz#quri_parts.openfermion.ansatz.TrotterUCCSD)                                                            | No       | True    | True    |\n",
+    "\n",
+    "A dictionary for running QURI Chemistry with UCCSD:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4ecca6f2-8fc0-4891-a80b-833680ebe384",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qsci_uccsd_json = {\n",
+    "    \"mole\": mole,\n",
+    "    \"ansatz\": \"UCCSD\",\n",
+    "    \"state_prep_method\": \"CCSD\",    # this is optional and defaulted to \"CCSD\".\n",
+    "    \"ansatz_setting\": {\"use_singles\": False},   # this is optional and defaulted to the list above.\n",
+    "    \"qsci_setting\": {\"n_shots\": 1e5, \"number_of_states_pick_out\": 1e4},\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4bc6c868-a633-44c7-b2fd-3c2bc9ccd2fb",
+   "metadata": {},
+   "source": [
+    "#### QURI Chemistry with double excitation ansatz\n",
+    "\n",
+    "When `DoubleExcitation` is chosen as the ansatz, you can specify how many excitation amplitudes to include with the `n_amplitudes` key in the `ansatz_setting` field. The default value is 10. The function sorts the CCSD amplitudes according to their magnitude and chooses the largest `n_amplitudes`  for constructing the ansatz. An example JSON dictionary using this ansatz follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ca296920-dd09-4666-9bb5-fef16d941607",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qsci_double_exc_json = {\n",
+    "    \"mole\": mole,\n",
+    "    \"ansatz\": \"DoubleExcitation\",\n",
+    "    \"state_prep_method\": \"CCSD\",    # this is optional and defaulted to \"CCSD\".\n",
+    "    \"ansatz_setting\": {\"n_amplitudes\": 20}, # this is optional and defaulted to {\"n_amplitudes\": 10}.\n",
+    "    \"qsci_setting\": {\"n_shots\": 1e5, \"number_of_states_pick_out\": 5e4},\n",
+    "    \"mitigation_setting\": {     # please refer to the \"error mitigation\" section of this tutorial.\n",
+    "        \"configuration_recovery\": {\n",
+    "            \"number_of_states_pick_out\": 10000\n",
+    "        }\n",
+    "    }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "320a1299-6b7e-458f-88e9-9e16426a7770",
+   "metadata": {},
+   "source": [
+    "#### Execute QSCI\n",
+    "\n",
+    "Example of running the `QSCI` algorithm:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "73b84c3b-f1b7-4032-b148-394967a8d1d7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2024-08-27 23:01:36.090958 QUEUED\n",
+      "2024-08-27 23:01:49.358961 QUEUED\n",
+      "2024-08-27 23:02:02.713788 QUEUED\n",
+      "2024-08-27 23:02:15.481531 RUNNING\n",
+      "2024-08-27 23:02:28.200317 RUNNING\n",
+      "2024-08-27 23:02:41.446378 RUNNING\n",
+      "2024-08-27 23:02:54.563686 RUNNING\n",
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+      "2024-08-27 23:04:12.528769 RUNNING\n",
+      "2024-08-27 23:04:25.625868 RUNNING\n",
+      "2024-08-27 23:04:38.718299 RUNNING\n",
+      "2024-08-27 23:04:51.828514 RUNNING\n",
+      "2024-08-27 23:05:04.745142 RUNNING\n",
+      "2024-08-27 23:05:17.727985 RUNNING\n",
+      "2024-08-27 23:05:31.061746 RUNNING\n",
+      "2024-08-27 23:05:43.821970 RUNNING\n",
+      "2024-08-27 23:05:56.897187 RUNNING\n",
+      "2024-08-27 23:06:10.088404 RUNNING\n",
+      "2024-08-27 23:06:22.959995 RUNNING\n",
+      "2024-08-27 23:06:35.873720 RUNNING\n",
+      "2024-08-27 23:06:48.798231 RUNNING\n",
+      "2024-08-27 23:07:02.731781 RUNNING\n",
+      "2024-08-27 23:07:15.819290 RUNNING\n",
+      "2024-08-27 23:07:29.360154 RUNNING\n",
+      "2024-08-27 23:07:42.341557 RUNNING\n",
+      "2024-08-27 23:07:55.720916 RUNNING\n",
+      "2024-08-27 23:08:09.072428 RUNNING\n",
+      "2024-08-27 23:08:22.077443 RUNNING\n",
+      "2024-08-27 23:08:35.038400 RUNNING\n",
+      "2024-08-27 23:08:47.958711 RUNNING\n",
+      "2024-08-27 23:09:00.828232 RUNNING\n",
+      "2024-08-27 23:09:14.713651 RUNNING\n",
+      "2024-08-27 23:09:28.076111 RUNNING\n",
+      "2024-08-27 23:09:41.481971 RUNNING\n",
+      "2024-08-27 23:09:54.650976 RUNNING\n",
+      "2024-08-27 23:10:07.775569 RUNNING\n",
+      "2024-08-27 23:10:20.951318 RUNNING\n",
+      "2024-08-27 23:10:33.815651 RUNNING\n",
+      "2024-08-27 23:10:46.684406 RUNNING\n"
+     ]
+    }
+   ],
+   "source": [
+    "import datetime\n",
+    "import time\n",
+    "job = function.run(\n",
+    "    method=\"QSCI\",\n",
+    "    parameters=qsci_double_exc_json,\n",
+    "    instance=\"INSTANCE\",\n",
+    "    backend=\"ibm_strasbourg\",\n",
+    ")\n",
+    "\n",
+    "while True:\n",
+    "    try:\n",
+    "        print(datetime.datetime.now(), job.status())\n",
+    "        time.sleep(10)\n",
+    "        if job.status() in [\"ERROR\", \"DONE\"]:\n",
+    "            break\n",
+    "    except:\n",
+    "        print(\"Retrieve job status failed.\")\n",
+    "        pass\n",
+    "\n",
+    "result = job.result()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59ff8f79-9dd4-41f4-84e8-560b49ede140",
+   "metadata": {},
+   "source": [
+    "You can print out the result from the run:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "3f9789e5-d9ca-489e-8333-20af04a43e3a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'qsci_energy': -76.05077853515165, 'state_vector': {'bits': [1023, 131583, 3039, 3063, 2943, 33663, 263163, 9087, 2043, 33759, 66303, 525183, 263151, 17403, 2031, 525303, 263103, 9207, 1983, 5115, 33783, 9183, 5055, 5103, 17343, 17391, 525279, 139647, 70383, 18411, 67311, 164319, 139767, 655839, 164343, 655743, 267243, 527319, 6123, 533367, 133623, 11127, 41847, 279531, 82683, 67263, 533343, 82671, 133503, 264171, 11103, 35703, 41823, 67323, 527199, 164223, 133599, 35679, 393855, 328383, 533463, 264111, 328431, 279483, 267183, 132735, 21483, 139743, 557919, 328443, 265083, 18351, 41943, 35799, 132591, 557943, 141687, 11223, 197595, 21423, 50043, 527223, 655863, 21435, 279471, 558039, 82623, 6075, 6063, 98943, 70335, 70395, 141663, 25455, 265179, 10107, 6399, 267195, 393723, 148191, 197583, 197439, 7131, 18363, 295923, 3963, 535383, 265203, 25407, 526239, 329403, 264447, 590331, 196863, 270591, 688503, 526287, 344763, 25467, 19359, 787323, 148095, 132855, 43863, 34047, 393951, 265071, 197619, 197535, 83691, 264123, 166263, 787407, 4047, 19323, 86763, 74463, 541563, 7155, 174423, 393975, 34803, 19263, 265023, 526191, 271335, 265119, 10227, 13179, 541551, 34767, 74367, 166239, 3903, 7035, 99039, 71355, 71343, 68343, 135675, 295911, 148215, 295803, 271227, 657759, 541659, 332523, 135807, 526299, 295791, 566103, 688479, 526203, 37875, 787431, 6975, 98751, 3327, 13215, 68091, 19419, 590463, 271167, 197607, 271347, 528639, 526323, 7023, 74487, 657879, 132831, 166359, 22443, 74235, 74223, 786687, 329391, 393711, 132543, 590319, 271215, 7143, 230247, 132603, 197487, 34623, 83643, 657783, 295743, 265143, 3999, 329451, 590271, 68319, 147963, 49983, 265191, 172503, 10047, 295887, 197559, 71403, 49407, 34671, 135927, 529275, 10191, 9471, 590559, 4071, 19311, 787419, 213963, 265167, 393663, 280491, 135903, 10095, 147903, 13167, 34743, 18687, 147951, 13275, 24831, 4083, 68223, 787359, 13299, 135663, 98799, 68031, 19407, 690519, 98811, 10215, 271263, 663927, 664023, 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+     ]
+    }
+   ],
+   "source": [
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff801eb7-24ec-41af-a516-8929b047d644",
+   "metadata": {},
+   "source": [
+    "Use the \\\"qsci_energy\\\" key to see the QSCI energy:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "c174b717-20ea-4354-8c43-5ae449eafbf9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CASCI energy: -76.0508056368444 Ha\n",
+      "QSCI energy: -76.05077853515165 Ha\n",
+      "Energy error:  2.71e-02 mHa\n"
+     ]
+    }
+   ],
+   "source": [
+    "CASCI_ENERGY = -76.0508056368444    # Exact result\n",
+    "qsci_energy = result[\"qsci_energy\"]\n",
+    "print(f\"CASCI energy: {CASCI_ENERGY} Ha\")\n",
+    "print(f\"QSCI energy: {qsci_energy} Ha\")\n",
+    "print(f\"Energy error: {(qsci_energy - CASCI_ENERGY) * 1000: .2e} mHa\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11d15237-66c0-4299-bd05-496516b0d577",
+   "metadata": {},
+   "source": [
+    "The returned ground state energy estimation of an $\\text{H}_2 \\text{O}$ molecule is very close to the exact result!\n",
+    "\n",
+    "The `state_vector` field contains three values: `bits`, `reals`, and `imags`. The `bits` value is a list of integers representing electron configurations. The $i$-th position of the `reals` (`imags`) field represents the real (imaginary) part of the amplitude of the electron configuration on position $i$. You can sort it by magnitude and print out the top 10 most important electron configurations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "4efcbff1-23fe-4a43-b449-bdddd9b532dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Electron configuration: 00000000001111111111, amplitude: -7.671e-01+6.185e-01j, magnitude:  9.710e-01\n",
+      "Electron configuration: 00110000000011111111, amplitude:  4.492e-02-3.622e-02j, magnitude:  3.330e-03\n",
+      "Electron configuration: 00000011001111001111, amplitude:  3.379e-02-2.724e-02j, magnitude:  1.884e-03\n",
+      "Electron configuration: 00001100001111001111, amplitude:  2.247e-02-1.812e-02j, magnitude:  8.333e-04\n",
+      "Electron configuration: 00100001000111101111, amplitude: -2.172e-02+1.751e-02j, magnitude:  7.780e-04\n",
+      "Electron configuration: 00010010001011011111, amplitude: -2.171e-02+1.750e-02j, magnitude:  7.775e-04\n",
+      "Electron configuration: 00000001101101101111, amplitude: -2.006e-02+1.617e-02j, magnitude:  6.637e-04\n",
+      "Electron configuration: 00000010011110011111, amplitude: -2.005e-02+1.617e-02j, magnitude:  6.636e-04\n",
+      "Electron configuration: 00000000111100111111, amplitude:  1.980e-02-1.596e-02j, magnitude:  6.467e-04\n",
+      "Electron configuration: 11000000001111001111, amplitude:  1.950e-02-1.572e-02j, magnitude:  6.275e-04\n",
+      "Electron configuration: 00010001001011101111, amplitude:  1.782e-02-1.437e-02j, magnitude:  5.242e-04\n",
+      "Electron configuration: 00100010000111011111, amplitude:  1.781e-02-1.436e-02j, magnitude:  5.233e-04\n",
+      "Electron configuration: 00000000111111001111, amplitude:  1.632e-02-1.316e-02j, magnitude:  4.396e-04\n",
+      "Electron configuration: 00010000101011110111, amplitude: -1.592e-02+1.284e-02j, magnitude:  4.183e-04\n",
+      "Electron configuration: 00100000010111111011, amplitude: -1.592e-02+1.284e-02j, magnitude:  4.183e-04\n",
+      "Electron configuration: 11000000001100111111, amplitude:  1.590e-02-1.282e-02j, magnitude:  4.171e-04\n",
+      "Electron configuration: 10000001001101101111, amplitude: -1.544e-02+1.245e-02j, magnitude:  3.935e-04\n",
+      "Electron configuration: 01000010001110011111, amplitude: -1.542e-02+1.243e-02j, magnitude:  3.925e-04\n",
+      "Electron configuration: 01100000000110111111, amplitude: -1.534e-02+1.237e-02j, magnitude:  3.882e-04\n",
+      "Electron configuration: 10010000001001111111, amplitude: -1.532e-02+1.235e-02j, magnitude:  3.874e-04\n",
+      "Electron configuration: 00001001001111001111, amplitude: -1.439e-02+1.160e-02j, magnitude:  3.418e-04\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "amps = np.array(result[\"state_vector\"][\"reals\"]) + 1j * np.array(result[\"state_vector\"][\"imags\"])\n",
+    "sort_idx = np.argsort(np.abs(amps))\n",
+    "\n",
+    "for i, (bit, real, imag) in enumerate(zip(\n",
+    "    np.array(result[\"state_vector\"][\"bits\"])[sort_idx][::-1],\n",
+    "    np.array(result[\"state_vector\"][\"reals\"])[sort_idx][::-1],\n",
+    "    np.array(result[\"state_vector\"][\"imags\"])[sort_idx][::-1],\n",
+    ")):\n",
+    "    if i > 20:\n",
+    "        break\n",
+    "    print(\n",
+    "        f\"Electron configuration: {bin(bit)[2:].zfill(20)}, \"\n",
+    "        f\"amplitude: {real + 1j * imag: .3e}, \"\n",
+    "        f\"magnitude: {real**2 + imag**2: .3e}\"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d35bc78-ddf3-47d0-9524-e32cd3d2dc42",
+   "metadata": {},
+   "source": [
+    "### Optimization-based QSCI\n",
+    "\n",
+    "If you don't want to use CCSD as the state preparation method, or you want to optimize further from the CCSD initial state, you can use the optimization-based QSCI. This algorithm gives you access to more chemistry-inspired ansatz with random initial parameters. The following is a table of ansatz and their corresponding supported state preparation methods:\n",
+    "\n",
+    "\n",
+    "| Ansatz                | Preparation method    |   Ansatz Setting Default  | Note                                                  |\n",
+    "| --------------------- | --------------------- | ------------------------- | ----------------------------------------------------- |\n",
+    "| Double Excitation     | CCSD                  | `{n_amplitudes: 10}`          | Lets you optimize from the result in the last example    |\n",
+    "| UCCSD                 | CCSD (default), Random          | `{use_singles: True, n_trotter: 1, reduce_parameter: True}` | Not recommended for systems with eight or more qubits      |\n",
+    "| KuCJ                  | Random                | `{k: 1}`                          | Recommended for system up to 16 qubits                 |\n",
+    "| KUpCCGSD              | Random                | `{k: 1, n_trotter: 1, reduce_parameter: False} `                         |                                                       |\n",
+    "| Hardware Efficient    | Random                | `{n_layers: depend on qubit size}        `                  |                                                       |\n",
+    "\n",
+    "If you don't specify the preparation method and the ansatz setting, default values are used. For this algorithm, the QSCI energy is used as the cost function for the COBYLA optimizer. In each iteration, the circuit parameters are updated and the QSCI energy is evaluated with the circuit carrying the new set of circuit parameters.\n",
+    "\n",
+    "The JSON dictionary used to run this algorithm is similar to that of QSCI. It contains one additional field, \"max_iter\", for limiting the resource usage. The default is 2000 iterations.\n",
+    "\n",
+    "The follwing is an example of running this algorithm with the 1-uCJ ansatz:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "2ba81e9e-1dfe-4a74-88ba-7a1a0fc0338f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "opt_qsci_1ucj_json = {\n",
+    "    \"mole\": mole,\n",
+    "    \"ansatz\": \"KuCJ\",\n",
+    "    \"state_prep_method\": \"RANDOM\",  # this is optional and defaulted to \"RANDOM\" for \"KuCJ\".\n",
+    "    \"ansatz_setting\": {\"k\": 1},     # this is optional and defaulted to the table above.\n",
+    "    \"qsci_setting\": {\n",
+    "        \"n_shots\": 1e5, \"number_of_states_pick_out\": 5e4\n",
+    "    },\n",
+    "    \"max_iter\": 5,                  # Default to 2000. Choose 5 iteration for demonstration.\n",
+    "    \"mitigation_setting\": {         # Refer to the \"error mitigation\" section.\n",
+    "        \"configuration_recovery\": {\n",
+    "            \"number_of_states_pick_out\": 10000\n",
+    "        }\n",
+    "    }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c84cd2d7-88ea-43eb-866d-039b047e0287",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "opt_job = function.run(\n",
+    "    method=\"OPT_QSCI\",\n",
+    "    parameters=\"opt_qsci_1ucj_json\",\n",
+    "    instance=\"ibm_strasbourg\",\n",
+    ")\n",
+    "start = time.time()\n",
+    "\n",
+    "while True:\n",
+    "    try:\n",
+    "        print(datetime.datetime.now(), opt_job.status())\n",
+    "        time.sleep(10)\n",
+    "        if opt_job.status() in [\"ERROR\", \"DONE\"]:\n",
+    "            break\n",
+    "    except:\n",
+    "        print(\"Retrieve job status failed.\")\n",
+    "        pass\n",
+    "\n",
+    "opt_result = opt_job.result()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5cc7a706-94cc-487d-bae7-aafe25590173",
+   "metadata": {},
+   "source": [
+    "You can print out the result."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "3ee83200-6f83-44af-a729-8d68f1311158",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'qsci_energy': -76.05051672600099, 'state_vector': {'bits': [1023, 1983, 2943, 33783, 3039, 263151, 33663, 2031, 5103, 9087, 525183, 33759, 2043, 17343, 9207, 9213, 525279, 17406, 131583, 5115, 525303, 263163, 525309, 17391, 9183, 3063, 263103, 66303, 3069, 2046, 17403, 263166, 5118, 33789, 5055, 67323, 133503, 70335, 264123, 655863, 67263, 279471, 139743, 6138, 67311, 655743, 133599, 139647, 11127, 164223, 267195, 527223, 18411, 18426, 533343, 558039, 82671, 21486, 264111, 267183, 11223, 328443, 328446, 70383, 328431, 35799, 533373, 6063, 527199, 11103, 6126, 21483, 533469, 18363, 279534, 67326, 41847, 533463, 279486, 655839, 533367, 133629, 70398, 11229, 21498, 558045, 11133, 82683, 21423, 41823, 139767, 527319, 35679, 557919, 264126, 164343, 10095, 35703, 267243, 655869, 267198, 21435, 328383, 279531, 527325, 271356, 6075, 18366, 83631, 164349, 264171, 557943, 271311, 83691, 139773, 527229, 6078, 133623, 13215, 82686, 6123, 68319, 41949, 279546, 35805, 21438, 18414, 270591, 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+     ]
+    }
+   ],
+   "source": [
+    "print(opt_result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0769995f-8cfa-48e9-a429-57c2b50cd03b",
+   "metadata": {},
+   "source": [
+    "The returned result has the same fields as the one in the QSCI example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "88062676-c6e3-40bf-8a62-d527e939f9e2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CASCI energy: -76.0508056368444 Ha\n",
+      "QSCI energy: -76.05051672600099 Ha\n",
+      "Energy error:  2.89e-01 mHa\n",
+      "\n",
+      "Top 20 important configurations:\n",
+      "Electron configuration: 00000000001111111111, amplitude: -1.934e-01-9.664e-01j, magnitude:  9.713e-01\n",
+      "Electron configuration: 00110000000011111111, amplitude:  1.127e-02+5.629e-02j, magnitude:  3.295e-03\n",
+      "Electron configuration: 00000011001111001111, amplitude:  8.434e-03+4.214e-02j, magnitude:  1.847e-03\n",
+      "Electron configuration: 00001100001111001111, amplitude:  5.640e-03+2.818e-02j, magnitude:  8.258e-04\n",
+      "Electron configuration: 00010010001011011111, amplitude: -5.445e-03-2.721e-02j, magnitude:  7.698e-04\n",
+      "Electron configuration: 00100001000111101111, amplitude: -5.418e-03-2.707e-02j, magnitude:  7.622e-04\n",
+      "Electron configuration: 00000010011110011111, amplitude: -5.053e-03-2.524e-02j, magnitude:  6.628e-04\n",
+      "Electron configuration: 00000001101101101111, amplitude: -5.023e-03-2.510e-02j, magnitude:  6.551e-04\n",
+      "Electron configuration: 00000000111100111111, amplitude:  4.978e-03+2.487e-02j, magnitude:  6.434e-04\n",
+      "Electron configuration: 11000000001111001111, amplitude:  4.918e-03+2.457e-02j, magnitude:  6.280e-04\n",
+      "Electron configuration: 00100010000111011111, amplitude:  4.486e-03+2.241e-02j, magnitude:  5.224e-04\n",
+      "Electron configuration: 00010001001011101111, amplitude:  4.458e-03+2.227e-02j, magnitude:  5.160e-04\n",
+      "Electron configuration: 00000000111111001111, amplitude:  4.109e-03+2.053e-02j, magnitude:  4.383e-04\n",
+      "Electron configuration: 00010000101011110111, amplitude: -4.008e-03-2.002e-02j, magnitude:  4.170e-04\n",
+      "Electron configuration: 11000000001100111111, amplitude:  4.006e-03+2.001e-02j, magnitude:  4.166e-04\n",
+      "Electron configuration: 00100000010111111011, amplitude: -4.002e-03-1.999e-02j, magnitude:  4.157e-04\n",
+      "Electron configuration: 01000010001110011111, amplitude: -3.889e-03-1.943e-02j, magnitude:  3.927e-04\n",
+      "Electron configuration: 01100000000110111111, amplitude: -3.856e-03-1.927e-02j, magnitude:  3.860e-04\n",
+      "Electron configuration: 10010000001001111111, amplitude: -3.856e-03-1.927e-02j, magnitude:  3.860e-04\n",
+      "Electron configuration: 10000001001101101111, amplitude: -3.848e-03-1.923e-02j, magnitude:  3.845e-04\n",
+      "Electron configuration: 00000110001111001111, amplitude:  3.611e-03+1.804e-02j, magnitude:  3.386e-04\n"
+     ]
+    }
+   ],
+   "source": [
+    "CASCI_ENERGY = -76.0508056368444    # Exact result\n",
+    "opt_qsci_energy = opt_result[\"qsci_energy\"]\n",
+    "print(f\"CASCI energy: {CASCI_ENERGY} Ha\")\n",
+    "print(f\"QSCI energy: {opt_qsci_energy} Ha\")\n",
+    "print(f\"Energy error: {(opt_qsci_energy - CASCI_ENERGY) * 1000: .2e} mHa\")\n",
+    "print()\n",
+    "print(\"Top 20 important configurations:\")\n",
+    "amps = np.array(opt_result[\"state_vector\"][\"reals\"]) + 1j * np.array(opt_result[\"state_vector\"][\"imags\"])\n",
+    "sort_idx = np.argsort(np.abs(amps))\n",
+    "\n",
+    "for i, (bit, real, imag) in enumerate(zip(\n",
+    "    np.array(opt_result[\"state_vector\"][\"bits\"])[sort_idx][::-1],\n",
+    "    np.array(opt_result[\"state_vector\"][\"reals\"])[sort_idx][::-1],\n",
+    "    np.array(opt_result[\"state_vector\"][\"imags\"])[sort_idx][::-1],\n",
+    ")):\n",
+    "    if i > 20:\n",
+    "        break\n",
+    "    print(\n",
+    "        f\"Electron configuration: {bin(bit)[2:].zfill(20)}, \"\n",
+    "        f\"amplitude: {real + 1j * imag: .3e}, \"\n",
+    "        f\"magnitude: {real**2 + imag**2: .3e}\"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a86168d-2997-4c45-bd20-e10060492393",
+   "metadata": {},
+   "source": [
+    "## Error mitigation\n",
+    "\n",
+    "Usually, the problem at hand needs to respect some symmetry, especially particle number and spin conservation. To restore symmetry, use symmetry post-selection or [configuration recovery](https://arxiv.org/pdf/2405.05068v1) error mitigation. You can choose either of them with the \\\"mitigation_setting\\\" field in either algorithm. Symmetry post-selection is the default value.\n",
+    "\n",
+    "Post-selection removes the erroneous states completely, leaving only the basis states to construct the subspace Hamiltonian. This usually saves classical resources, but might miss many states. Configuration recovery attempts to flip individual bits in erroneous basis states with respect to some probability distribution. Using configuration recovery increases the number of basis states to build a larger subspace Hamiltonian, but it uses a lot more classical resources.\n",
+    "\n",
+    "\n",
+    "| Name                     | Type                        | Description                                                                                                                             | Required | Default |\n",
+    "| :----------------------- | :-------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------- | :------- | :------ |\n",
+    "| particle_number_symmetry | bool                        | If True, remove bitstrings that do not respect particle number conservation from sampling result                                    | No       | TRUE    |\n",
+    "| spin_symmetry            | bool                        | If True, remove bitstrings that do not respect $s_z$ conservation from sampling result.                                              | No       | TRUE    |\n",
+    "| configuration_recovery   | Union[bool, dict[str, int]] | Activate configuration recovery if set to values other than False. Refer to the configuration recovery section for dict[str, int] input | No       | FALSE   |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a4eb76c3-c367-4555-8130-60b21fbffd87",
+   "metadata": {},
+   "source": [
+    "### Symmetry post-selection\n",
+    "\n",
+    "These keys control the symmetry post-selection: `particle_number_symmetry` and `spin_symmetry`. Setting `particle_number_symmetry` to True selects those states that conserve the total electron numbers.  Setting `spin_symmetry` to True selects those states that conserve the $s_z$ quantum number. Currently, only $s_z = 0$ is supported. The default is:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0049320e-e93f-4233-8e16-8ed9586b4b2f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mitigation_setting = {\n",
+    "    \"particle_number_symmetry\": True,\n",
+    "    \"spin_symmetry\": True,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "443f26b5-c956-4613-b47e-8675a9dbec2d",
+   "metadata": {},
+   "source": [
+    "<Admonition type=\"note\">You cannot set spin symmetry to True when particle symmetry is False. </Admonition>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22b853a2-fa59-4bf6-9aa4-bc75ece900ec",
+   "metadata": {},
+   "source": [
+    "### Configuration recovery\n",
+    "\n",
+    "Instead of the symmetry post-selection, you can also choose to do configuration recovery. The most general sets of configuration recovery are summarized in the table below.\n",
+    "\n",
+    "| Name                      | Type        | Description                                                                                                          | Required | Default |\n",
+    "| :------------------------ | :---------- | :------------------------------------------------------------------------------------------------------------------- | :------- | :------ |\n",
+    "| n_recover_iteration       | int         | The number of recovery iterations                                                                                   | No       | 5       |\n",
+    "| n_batch                   | int         | The number of batches generated per iteration                                                                       | No       | 1       |\n",
+    "| number_of_states_pick_out | int or None | The dimension of the QSCI vector in the recovery process. If None, it is set to the same as the value used for QSCI | No       | None    |\n",
+    "| recovery_r_multiple       | int         | The multiplier for number_of_states_pick_out. This is the number of states selected from the raw sample             | No       | 10      |\n",
+    "\n",
+    "To activate configuration recovery, specify `configuration_recovery = True` in the error mitigation field, or customize any of the above fields. For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f78bd298-35de-4513-baa8-a0c5b3706815",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Use default configuration recovery setting\n",
+    "default_mitigation_setting = {\n",
+    "    \"mitigation_setting\": {\n",
+    "        \"configuration_recovery\": True\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "# Customized configuration recovery setting\n",
+    "customized_mitigation_setting = {\n",
+    "    \"mitigation_setting\": {\n",
+    "        \"configuration_recovery\": {\n",
+    "            \"n_recover_iteration\": 20,\n",
+    "            \"n_batch\": 10,\n",
+    "            \"number_of_states_pick_out\": 5000\n",
+    "        }\n",
+    "    }\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b09639e2-3d04-4b1d-be98-ae17432c1b4c",
+   "metadata": {},
+   "source": [
+    "<Admonition type=\"note\">You cannot enable any other mitigation methods when `configuration_recovery` is turned on.</Admonition>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9f9e75aa-cb2d-4225-96cf-343db6de6781",
+   "metadata": {},
+   "source": [
+    "## Performance\n",
+    "\n",
+    "### $\\text{N}_2$ dissociation curve\n",
+    "\n",
+    "As a performance benchmark, we provide the 20-qubit $\\text{N}_2$ dissociation curve as a demonstration.\n",
+    "\n",
+    "![N2_dissociation](/images/guides/qunasys-qsci/N2_dissociation.svg)\n",
+    "\n",
+    "The red curve in the plot is generated by the QSCI method with the chosen ansatz `DoubleExcitation`. You can reproduce it with the following code using different choices of `d`, the distance between the nitrogen atoms."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3773fafe-6a8e-4b3e-96de-57c9def4cae3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d = 1.0\n",
+    "n2_dissociation_qsci = {\n",
+    "    \"mole\": {\"atom\": f\"N 0 0 0; N 0 0 {d}\",},\n",
+    "    \"ansatz\": \"DoubleExcitation\",\n",
+    "    \"state_prep_method\": \"CCSD\",\n",
+    "    \"qsci_setting\": {\n",
+    "        \"n_shots\": 100000, \"number_of_states_pick_out\": 50000\n",
+    "    },\n",
+    "    \"mitigation_setting\": {\n",
+    "        \"configuration_recovery\": True\n",
+    "    },\n",
+    "}\n",
+    "job = function.run(\n",
+    "    method=\"QSCI\",\n",
+    "    parameters=n2_dissociation_qsci,\n",
+    "    backend=\"ibm_strasbourg\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dcb8f5a8-840d-4059-9a2a-8136c99ca3f5",
+   "metadata": {},
+   "source": [
+    "Note that each point typically takes about one minute of QPU usage. On the classical side, while $R$ is chosen to be 50000, it doesn't necessarily mean that diagonalization of a $50000 \\times 50000$ matrix is done. The number of samples after configuration recovery in this case is approximately 5000 to 7000, which sets the dimension of the subspace Hamiltonian. Thus, the classical diagonalization cost is much smaller than that of typical full configuration interaction (FCI) computation.\n",
+    "\n",
+    "The key takeaway of this result is that QSCI with suitable configuration recovery outperforms scalable classical methods such as Hartree-Fock (HF), CCSD, and CISD at _all_ choices of `d`. This implies that at a scale where FCI energy is no longer available, QSCI serves as a reliable method to estimate the ground state energy at all distances. For example, using the 6-31g basis,\n",
+    "a 36-qubit $\\text{N}_2$ dissociation curve produced by QURI Chemistry is given by\n",
+    "\n",
+    "![N2_dissociation](/images/guides/qunasys-qsci/36_qubit_N2_dissociation.svg)\n",
+    "\n",
+    "Here, the FCI curve is not available on a typical laptop (M2 Pro Chip, 16G RAM). However, QSCI can produce a qualitatively correct dissociation curve compared to the classical methods on the same machine, with the assistance of the `ibm_strasbourg` quantum computer."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "695b5649-7fe1-4809-86f3-9c8bb92fd64c",
+   "metadata": {},
+   "source": [
+    "### Azobenzene\n",
+    "\n",
+    "Azobenzene has two isomers, trans-azobenzene and cis-azobenzene. The energy difference between the ground states of the two isomers plays an important role in the photoisomerization of azobenzene. As an example, we benchmark the ground state energy difference between QSCI and FCI, with different active space settings up to 28 qubits. With configuration recovery, QURI Chemistry yields error under 5 mHa with only $10^5$ shots, 30 to 40 seconds of QPU usage, and under 10 minutes of classical post-processing time per point.\n",
+    "\n",
+    "![Azobenzene](/images/guides/qunasys-qsci/azobenzene.svg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d8837936-35d9-492a-b384-d3523f3f62c0",
+   "metadata": {},
+   "source": [
+    "## Support\n",
+    "\n",
+    "For molecules larger than 20 qubits, please contact [sales@qunasys.com](mailto:sales@qunasys.com)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c71a13cd-b0a1-4a6a-8c00-fbb8d4ecd507",
+   "metadata": {},
+   "source": [
+    "## Next steps\n",
+    "\n",
+    "<Admonition type=\"tip\" title=\"Recommendations\">\n",
+    "\n",
+    "- [Request access to QunaSys QURI Chemistry](https://quantum.ibm.com/functions?id=42f5a0ea-2c74-4681-a973-3ef97de97ee4)\n",
+    "- Try the [Compute dissociation curves for strong coupling systems with QunaSys QURI Chemistry](https://learning.quantum.ibm.com/tutorial/compute-dissociation-curves-for-strong-coupling-systems-with-quna-sys-qsci) tutorial.\n",
+    "\n",
+    "</Admonition>"
+   ]
+  }
+ ],
+ "metadata": {
+  "description": "This Qiskit Function for quantum chemistry calculation uses the QSCI algorithm and provides ground state energy estimation.",
+  "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"
+  },
+  "title": "QURI Chemistry - A Qiskit Function by QunaSys"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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"/>
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+</svg>
diff --git a/qiskit_bot.yaml b/qiskit_bot.yaml
index d0393107b0..58f2e5a59c 100644
--- a/qiskit_bot.yaml
+++ b/qiskit_bot.yaml
@@ -9,6 +9,9 @@ notification_prelude: |
 # we still want their name in the Qiskit Bot message so people know they are
 # relevant.
 notifications:
+  "docs/guides/addons":
+    - "@beckykd"
+    - "@jenglick"
   "docs/guides/ai-transpiler-passes":
     - "@cbjuan"
     - "@d-kremer"
diff --git a/scripts/config/cspell/dictionaries/people.txt b/scripts/config/cspell/dictionaries/people.txt
index af86a90e19..0b5e89c197 100644
--- a/scripts/config/cspell/dictionaries/people.txt
+++ b/scripts/config/cspell/dictionaries/people.txt
@@ -5,10 +5,10 @@ Ahokas
 Almaden
 Alon
 Ambainis
+Bajpe
 Boeblingen
 Bravyi
 Bremner
-Bajpe
 Bruhat
 Buratti
 Bures
@@ -23,13 +23,15 @@ Dueck
 Dupuis
 Easwar
 Eisert
+Fock
 Frobenius
+Golecha
 Gosset
 Gottesman
-Golecha
 Gushu
 Häner
 Hardamard
+Hartree
 Havlicek
 Hein
 Hiptmair
@@ -48,8 +50,8 @@ Kraus
 Kremer
 Kutin
 Lauer
-Luca
 Lindblad
+Luca
 Margolus
 Martonosi
 Maslov
@@ -73,13 +75,13 @@ Rebentrost
 Renato
 Renner
 Roetteler
-Rueschlikon
 Ruchir
+Rueschlikon
 Shaohan
-Siddharth
 Shaydulin
 Shende
 Shor’s
+Siddharth
 Simonetto
 Smolin
 Solovay
@@ -90,10 +92,10 @@ Tapp
 Toffoli
 Uhrig
 Vazirani
+Vedral
+Vishal
 Vishwakarma
 Viudes
-Vishal
-Vedral
 Watrous
 Weyl
 Woerner
diff --git a/scripts/config/cspell/dictionaries/qiskit.txt b/scripts/config/cspell/dictionaries/qiskit.txt
index 725331caeb..ec466772f1 100644
--- a/scripts/config/cspell/dictionaries/qiskit.txt
+++ b/scripts/config/cspell/dictionaries/qiskit.txt
@@ -1,6 +1,8 @@
 ALAP
 ASPLOS
 Abelian
+Addon
+Addons
 Algorithmiq
 Anharmonicity
 Ansatz
@@ -8,7 +10,6 @@ BFGS
 CDKM
 CHSH
 CNOT
-Colab
 CPLEX
 CPTP
 CSWAP
@@ -16,14 +17,19 @@ CVXPY
 Canonicalization
 Clbits
 Cliffords
+Colab
 DIMACS
+Deutsches
+Diagonalization
 EPLG
 Eigensolver
 Eigensolvers
+Elektronen
 Factorizers
 Fieldr
 Hadamard
 Hamiltonians
+IBMQPU
 ITTL
 Ipython
 MCMT
@@ -31,26 +37,24 @@ MCMTV
 NISQ
 PRNG
 Paulihedral
-pylatexenc
 QAOA
+QESEM
+QESEM's
 QPU's
 QRTE
+QURI
 QVSM
+Qedma
 Qiskit
 Qiskit
+Quna
 RCCX
 SLSQP
 SPSA
+Shinjo
 Symegine
-symplectic
-tensored
-uncompiled
-uncompressing
-uncompute
-uncomputed
-unentanglement
-unnormalized
 TOQM
+Transpilations
 Trotterization
 Trotterizations
 UCRX
@@ -104,7 +108,6 @@ fermionic
 fsim
 gphase
 ibmq
-IBMQPU
 initialised
 initialiser
 ints
@@ -114,6 +117,7 @@ iswap
 kwarg
 kwargs
 kwparams
+layerification
 lnot
 mathbb
 mathrm
@@ -136,6 +140,7 @@ pipx
 polynomially
 pydot
 pygments
+pylatexenc
 pyplot
 qarg
 qargs
@@ -178,6 +183,7 @@ toric
 transpiling
 uchannel
 uncompiled
+uncompressing
 uncompute
 uncomputed
 unentanglement
@@ -188,3 +194,5 @@ unphysical
 venv
 watsonx
 xgate
+textbf
+texttt
diff --git a/scripts/config/notebook-testing.toml b/scripts/config/notebook-testing.toml
index 2d96b30c66..560f2ef38d 100644
--- a/scripts/config/notebook-testing.toml
+++ b/scripts/config/notebook-testing.toml
@@ -35,6 +35,13 @@ notebooks_normal_test = [
 # Don't test the following notebooks (this section can include glob patterns)
 notebooks_exclude = [
     "**/.ipynb_checkpoints/**",
+    "docs/guides/qedma-qesem.ipynb",
+    "docs/guides/q-ctrl-optimization-solver.ipynb",
+    "docs/guides/q-ctrl-performance-management.ipynb",
+    "docs/guides/algorithmiq-tem.ipynb",
+    "docs/guides/functions.ipynb",
+    "docs/guides/qunasys-quri-chemistry.ipynb",
+    "docs/guides/ibm-circuit-function.ipynb",
 ]
 
 # The following notebooks submit jobs that can be mocked with a simulator
diff --git a/scripts/js/commands/checkPatternsIndex.ts b/scripts/js/commands/checkPatternsIndex.ts
index b638607486..c078848cdc 100644
--- a/scripts/js/commands/checkPatternsIndex.ts
+++ b/scripts/js/commands/checkPatternsIndex.ts
@@ -18,6 +18,7 @@ const IGNORED_URLS: string[] = [
   "/guides/qiskit-code-assistant",
   "/guides/qiskit-code-assistant-jupyterlab",
   "/guides/qiskit-code-assistant-vscode",
+  "/guides/addons",
 ];
 
 const INDEX_PAGES = [
diff --git a/scripts/js/commands/checkQiskitBotFiles.ts b/scripts/js/commands/checkQiskitBotFiles.ts
index ae7e6e72f4..c42467b0c5 100644
--- a/scripts/js/commands/checkQiskitBotFiles.ts
+++ b/scripts/js/commands/checkQiskitBotFiles.ts
@@ -43,6 +43,15 @@ const ALLOWED_OWNERLESS_FILES = new Set([
   "docs/migration-guides/qiskit-1.0",
   "docs/migration-guides/qiskit-1.0-features",
   "docs/migration-guides/qiskit-1.0-installation",
+  // TODO: add these to qiskit_bot.yaml once we confirm the stakeholders.
+  "docs/guides/algorithmiq-tem",
+  "docs/guides/functions",
+  "docs/guides/ibm-circuit-function",
+  "docs/guides/q-ctrl-optimization-solver",
+  "docs/guides/q-ctrl-performance-management",
+  "docs/guides/q-ctrl-performance-management",
+  "docs/guides/qedma-qesem",
+  "docs/guides/qunasys-quri-chemistry",
 ]);
 
 const ALLOWED_NONEXISTENT_FILES: Set<string> = new Set([]);
diff --git a/scripts/js/lib/links/ignores.ts b/scripts/js/lib/links/ignores.ts
index 392f2551ef..687cecc653 100644
--- a/scripts/js/lib/links/ignores.ts
+++ b/scripts/js/lib/links/ignores.ts
@@ -38,7 +38,11 @@ const ALWAYS_IGNORED_URLS__EXPECTED = [
   "mailto:tellibm@us.ibm.com",
 ];
 
-const ALWAYS_IGNORED_URLS__SHOULD_FIX: string[] = [];
+const ALWAYS_IGNORED_URLS__SHOULD_FIX: string[] = [
+  "mailto:qiskit_ibm@algorithmiq.fi",
+  "mailto:support@qedma.com",
+  "mailto:sales@qunasys.com",
+];
 
 export const ALWAYS_IGNORED_URLS = new Set([
   ...ALWAYS_IGNORED_URLS__EXPECTED,