diff --git a/binder-index.md b/binder-index.md
index a851276f87f1..c152e37daffb 100644
--- a/binder-index.md
+++ b/binder-index.md
@@ -272,6 +272,14 @@ These are noted in the README.md files for each sample, along with complete inst
diff --git a/samples/azure-quantum/qaoa/README.md b/samples/azure-quantum/qaoa/README.md
new file mode 100644
index 000000000000..005610d00bdd
--- /dev/null
+++ b/samples/azure-quantum/qaoa/README.md
@@ -0,0 +1,25 @@
+---
+page_type: sample
+author: KilianPoirier
+description: Introduction to QAOA using the OpenQAOA library.
+ms.author:
+ms.date:
+languages:
+- python
+products:
+- azure-quantum
+---
+
+# Solving Quadratic Unconstrained Binary Optimization (QUBO) problems using QAOA on Azure Quantum
+
+This sample shows how to solve quadratic unconstrained binary optimization problems using the Quantum Approximate Optimization Algorithm (QAOA) on the Azure Quantum service. It demonstrates how to operate the QAOA workflow with a readily available problem instance (Maximum Cut) as well as a general QUBO problem that can be taylored to other combinatorial problems like graph coloring or minimum vertex cover.
+
+## Manifest
+
+- [openqaoa.ipynb](./openqaoa.ipynb) Python notebook demonstrating how to run QAOA locally and on the Azure Quantum platform using the OpenQAOA package.
+- [openqaoa-recursive.ipynb](./openqaoa-recursive.ipynb) Python notebook demonstrating how to run RQAOA locally and on the Azure Quantum platform using the OpenQAOA package.
+
+## See Also
+
+To learn more about QAOA and how to solve QUBO problems using OpenQAOA, visit https://openqaoa.entropicalabs.com/
+This sample code and notebooks were written by members of Entropica Labs team.
\ No newline at end of file
diff --git a/samples/azure-quantum/qaoa/openqaoa-recursive.ipynb b/samples/azure-quantum/qaoa/openqaoa-recursive.ipynb
new file mode 100644
index 000000000000..bf95f81b2dff
--- /dev/null
+++ b/samples/azure-quantum/qaoa/openqaoa-recursive.ipynb
@@ -0,0 +1,746 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "536532e3-e7d1-4dd3-89bd-e42364e0f0a3",
+ "metadata": {},
+ "source": [
+ "# Recursive Quantum Approximate Optimization Algorithm"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c23791bb-879b-4ae1-85ca-b4ddf0678df2",
+ "metadata": {},
+ "source": [
+ "In this notebook, we provide a short introduction to recursive QAOA, and demonstrate how this technique is implemented in the OpenQAOA workflows by solving a fully-connected Hamiltonian with $\\pm 1$ weights."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "21db9274-9f0d-4056-91ac-d6675af70d22",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "### A brief introduction to RQAOA"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5e32b853-04df-4f17-b494-c8c6a6ff8a19",
+ "metadata": {},
+ "source": [
+ "Recursive QAOA (RQAOA) is an iterative variant of QAOA, first introduced by Bravyi et al. in [1] and further explored in [2,3]. \n",
+ "\n",
+ "This technique consists in recursively reducing the size of the problem by running QAOA. At each step, the QAOA output distribution is used to compute the expectation values \n",
+ "\n",
+ "$$\n",
+ "\\mathcal{M}_{i} = \\langle Z_{i} \\rangle \\qquad \\qquad \\qquad \\qquad \\qquad \\mathcal{M}_{ij} = \\langle Z_{i}Z_{j} \\rangle,\n",
+ "$$\n",
+ "\n",
+ "associated with the terms present in the Hamiltonian. Note that, by definition, these quantities are bounded between -1 and 1. The expectation values are then ranked according to their magnitude $|\\mathcal{M}_{(i),(ij)}|$, where we use $\\mathcal{M}_{(i),(ij)}$ to generically refer to both single- and two-spin expectation values. In its original formulation, the highest ranked value is selected. This value is then utilized to eliminate a qubit from the Hamiltonian, by imposing a constraint on the respective qubits, according to the nature of the highest ranked expectation value. The two kinds of constraints are\n",
+ "\n",
+ "$$\n",
+ "Z_{i} \\mapsto \\textrm{sign}(\\mathcal{M}_{(i)}) \\qquad \\qquad \\textrm{and} \\qquad \\qquad Z_{i} \\mapsto \\textrm{sign}(\\mathcal{M}_{(ij)}) Z_{j},\n",
+ "$$\n",
+ "\n",
+ "where the expectation value is rounded via the `sign` operation for consistency. The first one can be interpreted as fixing qubit $i$ to a specific state, $| 0 \\rangle$ if $\\textrm{sign}(\\mathcal{M}_{(i)}) > 0$ and $|1 \\rangle$ if $\\textrm{sign}(\\mathcal{M}_{(i)}) < 0$, and the second one as fixing qubit $i$ with respect to the configuration of $j$, i.e. $i$ and $j$ will be aligned if $\\textrm{sign}(\\mathcal{M}_{(ij)})> 0$ and antialigned otherwise. Inserting the correponding constraint directly into the Hamiltonian, we reduce the size of the problem by one qubit. Using the reduced Hamiltonian, QAOA is then run again and the same procedure is followed. Once the reduced problem reaches a predefined cutoff size $n_{\\textrm{cutoff}}$, it is solved exactly via classical methods. The final answer is then reconstructed by re-inserting the eliminated qubits into the classical solution following the appropriate order.\n",
+ "\n",
+ "In summary, the process is:\n",
+ "\n",
+ "1. Execute QAOA\n",
+ "2. Compute expectation values $\\mathcal{M}_{(i),(ij)}$ of terms present in the Hamiltonian\n",
+ "3. Rank expectation values according to their magnitude $|\\mathcal{M}_{(i),(ij)}|$\n",
+ "4. Select the expectation value with highest magnitude\n",
+ "5. Eliminate variable by imposing the appropriate constraint and obtain reduced problem\n",
+ "6. If new problem size is smaller than $n_{\\textrm{cutoff}}$, obtain final solution classically and reinsert constraints, else, return to step 1 using the reducedproblem\n",
+ "\n",
+ "This version of RQAOA is included in OpenQAOA. Additionally, OpenQAOA incorporates RQAOA from two different generalized version of these procedure, which enable multiple qubit eliminations during the recursive process, modifying steps 4 and 5 above. These strategies are denoted as `custom` and `adaptive` [4], in accordance with the precise concept under which the elimination method takes place. In a nutshell, they are described as follows:\n",
+ "\n",
+ "\n",
+ "* The ``custom`` strategy allows the user to define the number of eliminations to be performed at each step. This is defined by the parameter ``steps``. If the parameter is set as an integer, the algorithm will use this value as the number of qubits to be eliminated at each step. Alternatively, it is possible to pass a list, which specifies the number of qubits to be eliminated at each step. For ``steps = 1``, the algorithm reduces to the original form of RQAOA presented in [1].\n",
+ "\n",
+ "* The ``adaptive`` strategy adaptively selects how many qubits to eliminate at each step. The maximum number of allowed eliminations is given by the parameter ``n_max``. At each step, the algorithm selects the top ``n_max+1`` expectation values (ranked in magnitude), computes the mean among them, and uses the ones lying above it for qubit elimination. This corresponds to a maximum of ``n_max`` possible elimination per step. For ``n_max= 1``, the algorithm reduces to the original form of RQAOA presented in [1].\n",
+ "\n",
+ "**NOTE**: The specific performance of these generalizations is currently under investigation. In particular, the development of Adaptive RQAOA is associated with an internal research project at Entropica Labs to be released publicly in the near future [4]. We make these strategies already available to the community in order to strengthen the exploration of more complex elimination schemes for RQAOA, beyond its original formulation [1]."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "61270353-74c9-4163-a663-5c89d18976fc",
+ "metadata": {},
+ "source": [
+ "## References"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6534a736-093b-44d3-95b6-7a8fa309c2f8",
+ "metadata": {},
+ "source": [
+ "[1] S. Bravyi, A. Kliesch, R. Koenig, and E. Tang, [Physical Review Letters 125, 260505 (2020)](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.125.260505) \\\n",
+ "[2] S. Bravyi, A. Kliesch, R. Koenig, and E. Tang, [(2020), 10.22331/q-2022-03-30-678](https://quantum-journal.org/papers/q-2022-03-30-678/) \\\n",
+ "[3] D. J. Egger, J. Marecek, and S. Woerner, [Quantum 5, 479 (2021)](https://doi.org/10.22331/q-2021-06-17-479) \\\n",
+ "[4] E. I. RodrΓguez Chiacchio, V. Sharma, E. Munro (Work in progress) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "f1b38648-393a-4974-af43-a2c7d960fb17",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " import openqaoa_azure\n",
+ "except ImportError:\n",
+ " !pip -q install openqaoa-azure\n",
+ " import openqaoa_azure"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "01bd94e3-7f36-4c38-85a8-f85283804369",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import networkx as nx\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "from openqaoa import RQAOA, QUBO, create_device\n",
+ "from openqaoa.utilities import ground_state_hamiltonian, plot_graph\n",
+ "from openqaoa.qaoa_components import Hamiltonian"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b7d33cb7-0110-4278-993f-69b5c4defbb6",
+ "metadata": {},
+ "source": [
+ "## Setting the problem"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "be11f78a-d04d-46b3-891d-5b1e37e409fb",
+ "metadata": {},
+ "source": [
+ "We define our problem to be a fully-connected system, where we choose the couplings $J_{ij}$ to be of magnitude 1, but with a randomly assigned signs, and for simplicity we set linear terms to 0. The workflow requires us to define the problem as an instance of the ``QUBO`` (Quadratic Unconstrained Binary Optimization) class, which is easily done by defining the connectivity of the problem and the coupling values."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "1f9d971e-b5bc-4dcd-be67-f98742374570",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "