From 6b4f26ad42682e639edc774fe0cc2c099375c1fa Mon Sep 17 00:00:00 2001 From: Vishal Bajpe Date: Tue, 12 May 2026 02:58:22 +0800 Subject: [PATCH 1/4] Update spin chain vqe tutorial --- docs/tutorials/spin-chain-vqe.ipynb | 183 ++++++++++++++++++++-------- 1 file changed, 131 insertions(+), 52 deletions(-) diff --git a/docs/tutorials/spin-chain-vqe.ipynb b/docs/tutorials/spin-chain-vqe.ipynb index a23e8cd746e..44ae8d8cb2e 100644 --- a/docs/tutorials/spin-chain-vqe.ipynb +++ b/docs/tutorials/spin-chain-vqe.ipynb @@ -16,17 +16,40 @@ "\n", "# Ground-state energy estimation of the Heisenberg chain with VQE\n", "\n", - "*Usage estimate: 37 minutes on a Heron processor (NOTE: This is an estimate only. Your runtime might vary.)*" + "*Usage estimate: 37 minutes on a Heron processor (NOTE: This is an estimate only. Your runtime might vary.)*\n", + "\n" ] }, { "cell_type": "markdown", - "id": "49d868bf", + "id": "e28ad544", "metadata": {}, "source": [ + "## Learning outcomes\n", + "After completing this tutorial, you can expect to understand the following information:\n", + "- How to model a Heisenberg spin chain as a quantum Hamiltonian using Qiskit\n", + "- How to use the SPSA optimizer to estimate the ground-state energy of a quantum system\n", + "- How to execute variational workflows on IBM quantum hardware using Qiskit Runtime primitives and sessions\n", + "\n", + "## Prerequisites\n", + "It is recommended that you familiarize yourself with these topics:\n", + "- [Basics of quantum information](/learning/courses/basics-of-quantum-information)\n", + "- [Introduction to Qiskit patterns](/docs/guides/intro-to-patterns)\n", + "- [Variational algorithm design](/learning/courses/variational-algorithm-design)\n", + "\n", "## Background\n", "\n", - "This tutorial shows how to build, deploy, and run a development workflow called a [Qiskit pattern](/docs/guides/intro-to-patterns) for simulating a Heisenberg chain and estimating its ground-state energy using the SPSA optimizer." + "The Heisenberg spin chain is one of the most widely studied models in condensed matter physics and quantum magnetism. It describes a one-dimensional lattice of interacting quantum spins, where nearest-neighbor spins are coupled through exchange interactions. The Hamiltonian for the isotropic Heisenberg model with an external magnetic field is given by:\n", + "\n", + "$$H = \\sum_{\\langle i,j \\rangle} \\left( J_x X_i X_j + J_y Y_i Y_j + J_z Z_i Z_j \\right) + \\sum_{i} h_i Z_i$$\n", + "\n", + "where $X_i$, $Y_i$, and $Z_i$ are the Pauli operators acting on site $i$, the sum $\\langle i,j \\rangle$ runs over nearest-neighbor pairs, $J_x = J_y = J_z = 0.5$ are the exchange coupling constants (isotropic in this tutorial), and $h_i$ represents a site-dependent external magnetic field. In this tutorial, the magnetic field values are randomly sampled from the interval $[-1, 1]$. Note that in the implementation below, the set of \"nearest-neighbor\" pairs is determined by the hardware backend's native coupling among the first $N$ qubits, which may not form a strict linear chain depending on the device topology.\n", + "\n", + "Understanding the ground-state energy of this Hamiltonian is of fundamental importance in physics. The ground state encodes information about quantum phase transitions, entanglement structure, and magnetic ordering. Classically, computing the exact ground-state energy becomes intractable as the number of spins grows, since the Hilbert space dimension scales exponentially as $2^N$ for $N$ spins. This makes it a natural candidate for quantum simulation.\n", + "\n", + "The Variational Quantum Eigensolver (VQE) is a hybrid quantum-classical algorithm designed to estimate the ground-state energy of a Hamiltonian. It works by preparing a parameterized quantum state $|\\psi(\\theta)\\rangle$ (called an ansatz) on a quantum computer and measuring the expectation value $\\langle \\psi(\\theta) | H | \\psi(\\theta) \\rangle$. A classical optimizer then iteratively adjusts the parameters $\\theta$ to minimize this energy, leveraging the variational principle which guarantees that the measured energy is always an upper bound to the true ground-state energy.\n", + "\n", + "In this tutorial, we use the `efficient_su2` ansatz from Qiskit's circuit library, which constructs layers of single-qubit rotations and entangling gates. The optimization is carried out using the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm, which is well-suited for noisy quantum hardware because it estimates gradients using only two function evaluations per iteration regardless of the number of parameters.\n" ] }, { @@ -38,8 +61,9 @@ "\n", "Before starting this tutorial, ensure that you have the following installed:\n", "\n", - "* Qiskit SDK v2.0 or later, with [visualization](/docs/api/qiskit/visualization) support\n", - "* Qiskit Runtime v0.44 or later (`pip install qiskit-ibm-runtime`)" + "* Qiskit SDK v2.0 or later, with [visualization](/docs/api/qiskit/visualization) support\n", + "* Qiskit Runtime v0.44 or later (`pip install qiskit-ibm-runtime`)\n", + "\n" ] }, { @@ -47,12 +71,13 @@ "id": "a46e9e3e", "metadata": {}, "source": [ - "## Setup" + "## Setup\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "id": "e7754922", "metadata": {}, "outputs": [], @@ -82,32 +107,44 @@ " plt.show()" ] }, + { + "cell_type": "markdown", + "id": "890d9c81", + "metadata": {}, + "source": [ + "## Small-scale example\n", + "\n", + "In this section, we walk through each step of the Qiskit pattern at a small scale, explaining the key components as we build up the workflow.\n" + ] + }, { "cell_type": "markdown", "id": "132fb15f-10b4-4d7e-83d8-f512a6f675d1", "metadata": {}, "source": [ - "## Step 1: Map classical inputs to a quantum problem\n", + "### Step 1: Map classical inputs to a quantum problem\n", "\n", - "* Input: Number of spins\n", - "* Output: Ansatz and Hamiltonian modeling the Heisenberg chain\n", + "* Input: Number of spins\n", + "* Output: Ansatz and Hamiltonian modeling the Heisenberg chain\n", "\n", - "Construct an ansatz and Hamiltonian that model a 10-spin Heisenberg chain. First, we import some generic packages and create some helper functions." + "Construct an ansatz and Hamiltonian that model a 10-spin Heisenberg chain. In this step, we will build a 10-spin Heisenberg Hamiltonian over the least-busy backend's coupling map and prepare the `efficient_su2` ansatz\n", + "\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "7e8d2f10-f1d6-4ec2-bac9-9db23499c9e1", "metadata": {}, "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
" ] }, - "execution_count": 8, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -116,11 +153,7 @@ "num_spins = 10\n", "ansatz = efficient_su2(num_qubits=num_spins, reps=2)\n", "\n", - "service = QiskitRuntimeService(\n", - " channel=\"ibm_cloud\",\n", - " token=\"\", # Replace with your actual API token\n", - " instance=\"\", # Replace with your instance name if needed\n", - ")\n", + "service = QiskitRuntimeService()\n", "backend = service.least_busy(\n", " operational=True, min_num_qubits=num_spins, simulator=False\n", ")\n", @@ -149,27 +182,29 @@ "id": "ab79119b-5e56-49d8-a20e-1c8e665baec0", "metadata": {}, "source": [ - "## Step 2: Optimize problem for quantum hardware execution\n", + "### Step 2: Optimize problem for quantum hardware execution\n", "\n", - "* Input: Abstract circuit, observable\n", - "* Output: Target circuit and observable, optimized for the selected QPU\n", + "* Input: Abstract circuit, observable\n", + "* Output: Target circuit and observable, optimized for the selected QPU\n", "\n", - "Use the `generate_preset_pass_manager` function from Qiskit to automatically generate an optimization routine for our circuit with respect to the selected QPU. We choose `optimization_level=3`, which provides the highest level of optimization of the preset pass managers. We also include `ALAPScheduleAnalysis` and `PadDynamicalDecoupling` scheduling passes to suppress decoherence errors." + "Use the `generate_preset_pass_manager` function from Qiskit to automatically generate an optimization routine for our circuit with respect to the selected QPU. We choose `optimization_level=3`, which provides the highest level of optimization of the preset pass managers. We also include `ALAPScheduleAnalysis` and `PadDynamicalDecoupling` scheduling passes to suppress decoherence errors.\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "id": "a0a5f1c8-5c31-4d9f-ae81-37bd67271d44", "metadata": {}, "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
" ] }, - "execution_count": 5, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -197,16 +232,17 @@ "id": "9e889d0b-30b5-4e6b-84c9-d1f096abf132", "metadata": {}, "source": [ - "## Step 3: Execute using Qiskit primitives\n", + "### Step 3: Execute using Qiskit primitives\n", "\n", - "* Input: Target circuit and observable\n", - "* Output: Results of optimization\n", + "* Input: Target circuit and observable\n", + "* Output: Results of optimization\n", "\n", "Minimize the estimated ground-state energy of the system by optimizing the circuit parameters. Use the `Estimator` primitive from Qiskit Runtime to evaluate the cost function during optimization.\n", "\n", "Since we optimized the circuit for the backend in Step 2, we can avoid doing transpilation on the Runtime server by setting `skip_transpilation=True` and passing the optimized circuit. For this demo, we will run on a QPU using `qiskit-ibm-runtime` primitives. To run with `qiskit` statevector-based primitives, replace the block of code using Qiskit Runtime primitives with the commented block.\n", "\n", - "In this tutorial we use Simultaneous Perturbation Stochastic Approximation (SPSA), which is a gradient-based optimizer. Next we give a brief introduction to it, and provide the code to implement SPSA using Qiskit v2.0." + "In this tutorial we use Simultaneous Perturbation Stochastic Approximation (SPSA), which is a gradient-based optimizer. Next we give a brief introduction to it, and provide the code to implement SPSA using Qiskit v2.0.\n", + "\n" ] }, { @@ -218,16 +254,22 @@ "\n", "Simultaneous Perturbation Stochastic Approximation (SPSA) [\\[1\\]](#references) is an optimization algorithm that approximates the entire gradient vector using only two function calls at each iteration. Let $f:\\mathbb{R}^p\\rightarrow \\mathbb{R}$ be the cost function with $p$ parameters to be optimized, and $x_i\\in \\mathbb{R}^p$ be the parameter vector at the $i^{th}$ step of the iteration. To compute the gradient, a random vector $\\Delta_i$ of size $p$ is created, where each element $\\Delta_{ij}$, $\\forall$ $j\\in \\{1,2,...,p\\}$, is uniformly sampled from $\\{-1, 1\\}$. Next, each element of the random vector $\\Delta_i$ is multiplied with a small value $c_i$ to create a random perturbation. The gradient is then estimated as\n", "\n", + "\n", "$$[\\nabla f(x_i)]_j \\approx \\frac{f(x_i + c_i \\Delta_i) - f(x_i - c_i \\Delta_i)}{2c_i\\Delta_{ij}}.$$\n", "\n", "Intuitively, since a random perturbation is applied during the gradient estimation, it is expected that small deviations in the exact values of $f$ coming from noise can be tolerated and accounted for. In fact, SPSA is particularly known to be robust against noise, and requires only two hardware calls for each iteration. It is, therefore, one of the highly preferred optimizers for implementing variational algorithms.\n", "\n", - "In this tutorial, the hyperparameters for the $i^{th}$ iteration, $a_i$ and $c_i$, are computed as $a_i = a/(A + i + 1)^\\alpha$ and $c_i = c / (i+1)^\\gamma$, where the constant values are taken as $A = 30$, $\\alpha = 0.9$, $a = 0.3$, $c = 0.1$, and $\\gamma = 0.4$. These values are selected from [\\[2\\]](#references). Appropriate tuning of hyperparameters is necessary for extracting a good performance out of SPSA." + "In this tutorial, the hyperparameters for the $i^{th}$ iteration, $a_i$ and $c_i$, are computed as \n", + "\n", + "$$a_i = \\frac{a}{(A + i + 1)^\\alpha} \\quad \\text{and} \\quad c_i = \\frac{c}{(i+1)^\\gamma}$$\n", + "\n", + "where the constant values are taken as $A = 30$, $\\alpha = 0.9$, $a = 0.3$, $c = 0.1$, and $\\gamma = 0.4$. These values are selected from [\\[2\\]](#references). Appropriate tuning of hyperparameters is necessary for extracting a good performance out of SPSA.\n", + "\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "73a9352c", "metadata": {}, "outputs": [], @@ -256,7 +298,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "32ca3b6a", "metadata": {}, "outputs": [], @@ -297,6 +339,7 @@ " with Session(backend=backend) as session:\n", " estimator = EstimatorV2(mode=session)\n", " estimator.skip_transpilation = True\n", + " estimator.options.environment.job_tags = [\"TUT_HSVQE\"]\n", " x_opt = spsa(\n", " cost_func,\n", " x0=x0,\n", @@ -313,7 +356,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "id": "f418b372", "metadata": {}, "outputs": [], @@ -328,12 +371,13 @@ "id": "3bf42923", "metadata": {}, "source": [ - "Here we set the `maxiter = 50`. Note that since each iteration requires two calls to the function to compute the gradient, the total number of function calls will be $2 \\times \\text{maxiter}$. The `maxiter` can be increased to any higher value for better energy estimation." + "Here we set the `maxiter = 50`. Note that since each iteration requires two calls to the function to compute the gradient, the total number of function calls will be $2 \\times \\text{maxiter}$. The `maxiter` can be increased to any higher value for better energy estimation.\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "id": "1732ce37", "metadata": {}, "outputs": [ @@ -341,7 +385,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Fx Iters. done: 101 [Current cost: -2.19621]\r" + "Fx Iters. done: 101 [Current cost: -3.03843]" ] } ], @@ -357,15 +401,16 @@ "id": "33abbb3f-6245-4610-a05d-e2bc4cc551f0", "metadata": {}, "source": [ - "## Step 4: Post-process and return result in desired classical format\n", + "### Step 4: Post-process and return result in desired classical format\n", "\n", - "* Input: Ground-state energy estimates during optimization\n", - "* Output: Estimated ground-state energy" + "* Input: Ground-state energy estimates during optimization\n", + "* Output: Estimated ground-state energy\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "id": "e5b58771-d543-4e75-9746-fbc7b28e4360", "metadata": {}, "outputs": [ @@ -373,7 +418,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Estimated ground state energy: [-2.19621239]\n" + "Estimated ground state energy: [-3.03842968]\n" ] } ], @@ -383,14 +428,15 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "ecd7762a", "metadata": {}, "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "\"Output" + "
" ] }, "metadata": {}, @@ -405,6 +451,22 @@ "visualize_results(spsa_history)" ] }, + { + "cell_type": "markdown", + "id": "a3f4171f", + "metadata": {}, + "source": [ + "## Large-scale hardware example\n" + ] + }, + { + "cell_type": "markdown", + "id": "746e6133", + "metadata": {}, + "source": [ + "A large-scale hardware example is not included in this tutorial. As the number of qubits increases, VQE encounters significant challenges due to the [barren plateau](https://quantum.cloud.ibm.com/learning/en/courses/variational-algorithm-design/optimization-loops#barren-plateaus) phenomenon: the gradient of the cost function vanishes exponentially with system size, making optimization practically infeasible for large circuits. Combined with hardware noise, this means that scaling VQE to larger spin chains does not produce reliably reproducible results. For approaches that overcome these limitations, see the Next Steps section below.\n" + ] + }, { "cell_type": "markdown", "id": "217a9379", @@ -412,10 +474,11 @@ "source": [ "## References\n", "\n", - "[1] Spall, J. C. (2002). Implementation of the simultaneous perturbation algorithm for stochastic optimization.\n", + "\\[1] Spall, J. C. (2002). Implementation of the simultaneous perturbation algorithm for stochastic optimization.\n", "IEEE Transactions on Aerospace and Electronic Systems, 34(3), 817-823.\n", "\n", - "[2] Sahin, M. Emre, et al. (2025). Qiskit Machine Learning: an open-source library for quantum machine learning tasks at scale on quantum hardware and classical simulators. arXiv:2505.17756." + "\\[2] Sahin, M. Emre, et al. (2025). Qiskit Machine Learning: an open-source library for quantum machine learning tasks at scale on quantum hardware and classical simulators. arXiv:2505.17756.\n", + "\n" ] }, { @@ -426,17 +489,33 @@ "## Next steps\n", "\n", "\n", - "If you found this work interesting, you might be interested in the following material:\n", + " If you found this work interesting, you might be interested in the following material:\n", + "\n", + " * **Try Sample-based Quantum Diagonalization (SQD):** As demonstrated in this tutorial, VQE faces challenges at scale due to barren plateaus and high measurement overhead. IBM has developed [Sample-based Quantum Diagonalization (SQD)](https://www.ibm.com/quantum/blog/quantum-diagonalization) as a more scalable alternative. Unlike VQE, SQD avoids variational optimization entirely — instead, a quantum computer generates samples and a classical computer projects the Hamiltonian onto a subspace spanned by those samples and diagonalizes it. This provides an upper bound to the ground-state energy with significantly fewer measurements and without susceptibility to barren plateaus. Follow the [SQD tutorial](/docs/tutorials/sample-based-quantum-diagonalization) to see this approach in action.\n", + " * **Explore the Quantum Diagonalization Algorithms course:** Deepen your understanding of both VQE and SQD, including their trade-offs, in the [Quantum Diagonalization Algorithms](/learning/courses/quantum-diagonalization-algorithms) course on IBM Quantum Learning.\n", + "\n", "\n", - "- Find the ground-state energy of a sparse Hamiltonian by following the [SQD tutorial](/docs/tutorials/sample-based-quantum-diagonalization)\n", - "- Take the [Variational algorithm design](/learning/courses/variational-algorithm-design) course in IBM Learning\n", - "" + "### Challenge\n", + "\n", + "Now that you have a working VQE implementation for the Heisenberg chain, try the following:\n", + "\n", + "1. **Experiment with ansatz depth:** Modify the `reps` parameter in `efficient_su2` (e.g., try `reps=1` and `reps=3`). How does ansatz depth affect the estimated ground-state energy and convergence speed? At what point do you observe diminishing returns or instability?\n", + "2. **Tune SPSA hyperparameters:** Adjust the learning rate schedule parameters (`a`, `c`, `alpha`, `gamma`, `A`) and observe how they impact convergence. Can you find a configuration that converges faster than the defaults used here?\n", + "3. **Compare coupling topologies:** Instead of using the backend's native coupling map, try constructing a simple nearest-neighbor linear chain and compare the results. How does the connectivity of the physical hardware affect the transpiled circuit depth and final energy estimate?\n" + ] + }, + { + "cell_type": "markdown", + "id": "a1b8767d", + "metadata": {}, + "source": [ + "© IBM Corp., 2017-2026" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -450,7 +529,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3" + "version": "3.12.10" } }, "nbformat": 4, From c3a220816bf3b915e2a73dc459303498048fd859 Mon Sep 17 00:00:00 2001 From: Henry Zou Date: Mon, 11 May 2026 15:30:42 -0400 Subject: [PATCH 2/4] tox -e fix --- docs/tutorials/spin-chain-vqe.ipynb | 55 +++++++----------- ...e8d2f10-f1d6-4ec2-bac9-9db23499c9e1-0.avif | Bin 10991 -> 13359 bytes ...0a5f1c8-5c31-4d9f-ae81-37bd67271d44-0.avif | Bin 16820 -> 20243 bytes .../extracted-outputs/ecd7762a-0.avif | Bin 4695 -> 4822 bytes 4 files changed, 21 insertions(+), 34 deletions(-) diff --git a/docs/tutorials/spin-chain-vqe.ipynb b/docs/tutorials/spin-chain-vqe.ipynb index 44ae8d8cb2e..ceed5a16934 100644 --- a/docs/tutorials/spin-chain-vqe.ipynb +++ b/docs/tutorials/spin-chain-vqe.ipynb @@ -16,8 +16,7 @@ "\n", "# Ground-state energy estimation of the Heisenberg chain with VQE\n", "\n", - "*Usage estimate: 37 minutes on a Heron processor (NOTE: This is an estimate only. Your runtime might vary.)*\n", - "\n" + "*Usage estimate: 37 minutes on a Heron processor (NOTE: This is an estimate only. Your runtime might vary.)*" ] }, { @@ -49,7 +48,7 @@ "\n", "The Variational Quantum Eigensolver (VQE) is a hybrid quantum-classical algorithm designed to estimate the ground-state energy of a Hamiltonian. It works by preparing a parameterized quantum state $|\\psi(\\theta)\\rangle$ (called an ansatz) on a quantum computer and measuring the expectation value $\\langle \\psi(\\theta) | H | \\psi(\\theta) \\rangle$. A classical optimizer then iteratively adjusts the parameters $\\theta$ to minimize this energy, leveraging the variational principle which guarantees that the measured energy is always an upper bound to the true ground-state energy.\n", "\n", - "In this tutorial, we use the `efficient_su2` ansatz from Qiskit's circuit library, which constructs layers of single-qubit rotations and entangling gates. The optimization is carried out using the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm, which is well-suited for noisy quantum hardware because it estimates gradients using only two function evaluations per iteration regardless of the number of parameters.\n" + "In this tutorial, we use the `efficient_su2` ansatz from Qiskit's circuit library, which constructs layers of single-qubit rotations and entangling gates. The optimization is carried out using the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm, which is well-suited for noisy quantum hardware because it estimates gradients using only two function evaluations per iteration regardless of the number of parameters." ] }, { @@ -62,8 +61,7 @@ "Before starting this tutorial, ensure that you have the following installed:\n", "\n", "* Qiskit SDK v2.0 or later, with [visualization](/docs/api/qiskit/visualization) support\n", - "* Qiskit Runtime v0.44 or later (`pip install qiskit-ibm-runtime`)\n", - "\n" + "* Qiskit Runtime v0.44 or later (`pip install qiskit-ibm-runtime`)" ] }, { @@ -71,8 +69,7 @@ "id": "a46e9e3e", "metadata": {}, "source": [ - "## Setup\n", - "\n" + "## Setup" ] }, { @@ -114,7 +111,7 @@ "source": [ "## Small-scale example\n", "\n", - "In this section, we walk through each step of the Qiskit pattern at a small scale, explaining the key components as we build up the workflow.\n" + "In this section, we walk through each step of the Qiskit pattern at a small scale, explaining the key components as we build up the workflow." ] }, { @@ -127,8 +124,7 @@ "* Input: Number of spins\n", "* Output: Ansatz and Hamiltonian modeling the Heisenberg chain\n", "\n", - "Construct an ansatz and Hamiltonian that model a 10-spin Heisenberg chain. In this step, we will build a 10-spin Heisenberg Hamiltonian over the least-busy backend's coupling map and prepare the `efficient_su2` ansatz\n", - "\n" + "Construct an ansatz and Hamiltonian that model a 10-spin Heisenberg chain. In this step, we will build a 10-spin Heisenberg Hamiltonian over the least-busy backend's coupling map and prepare the `efficient_su2` ansatz" ] }, { @@ -139,9 +135,8 @@ "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "
" + "\"Output" ] }, "execution_count": 2, @@ -187,8 +182,7 @@ "* Input: Abstract circuit, observable\n", "* Output: Target circuit and observable, optimized for the selected QPU\n", "\n", - "Use the `generate_preset_pass_manager` function from Qiskit to automatically generate an optimization routine for our circuit with respect to the selected QPU. We choose `optimization_level=3`, which provides the highest level of optimization of the preset pass managers. We also include `ALAPScheduleAnalysis` and `PadDynamicalDecoupling` scheduling passes to suppress decoherence errors.\n", - "\n" + "Use the `generate_preset_pass_manager` function from Qiskit to automatically generate an optimization routine for our circuit with respect to the selected QPU. We choose `optimization_level=3`, which provides the highest level of optimization of the preset pass managers. We also include `ALAPScheduleAnalysis` and `PadDynamicalDecoupling` scheduling passes to suppress decoherence errors." ] }, { @@ -199,9 +193,8 @@ "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "
" + "\"Output" ] }, "execution_count": 3, @@ -241,8 +234,7 @@ "\n", "Since we optimized the circuit for the backend in Step 2, we can avoid doing transpilation on the Runtime server by setting `skip_transpilation=True` and passing the optimized circuit. For this demo, we will run on a QPU using `qiskit-ibm-runtime` primitives. To run with `qiskit` statevector-based primitives, replace the block of code using Qiskit Runtime primitives with the commented block.\n", "\n", - "In this tutorial we use Simultaneous Perturbation Stochastic Approximation (SPSA), which is a gradient-based optimizer. Next we give a brief introduction to it, and provide the code to implement SPSA using Qiskit v2.0.\n", - "\n" + "In this tutorial we use Simultaneous Perturbation Stochastic Approximation (SPSA), which is a gradient-based optimizer. Next we give a brief introduction to it, and provide the code to implement SPSA using Qiskit v2.0." ] }, { @@ -259,12 +251,11 @@ "\n", "Intuitively, since a random perturbation is applied during the gradient estimation, it is expected that small deviations in the exact values of $f$ coming from noise can be tolerated and accounted for. In fact, SPSA is particularly known to be robust against noise, and requires only two hardware calls for each iteration. It is, therefore, one of the highly preferred optimizers for implementing variational algorithms.\n", "\n", - "In this tutorial, the hyperparameters for the $i^{th}$ iteration, $a_i$ and $c_i$, are computed as \n", + "In this tutorial, the hyperparameters for the $i^{th}$ iteration, $a_i$ and $c_i$, are computed as\n", "\n", "$$a_i = \\frac{a}{(A + i + 1)^\\alpha} \\quad \\text{and} \\quad c_i = \\frac{c}{(i+1)^\\gamma}$$\n", "\n", - "where the constant values are taken as $A = 30$, $\\alpha = 0.9$, $a = 0.3$, $c = 0.1$, and $\\gamma = 0.4$. These values are selected from [\\[2\\]](#references). Appropriate tuning of hyperparameters is necessary for extracting a good performance out of SPSA.\n", - "\n" + "where the constant values are taken as $A = 30$, $\\alpha = 0.9$, $a = 0.3$, $c = 0.1$, and $\\gamma = 0.4$. These values are selected from [\\[2\\]](#references). Appropriate tuning of hyperparameters is necessary for extracting a good performance out of SPSA." ] }, { @@ -371,8 +362,7 @@ "id": "3bf42923", "metadata": {}, "source": [ - "Here we set the `maxiter = 50`. Note that since each iteration requires two calls to the function to compute the gradient, the total number of function calls will be $2 \\times \\text{maxiter}$. The `maxiter` can be increased to any higher value for better energy estimation.\n", - "\n" + "Here we set the `maxiter = 50`. Note that since each iteration requires two calls to the function to compute the gradient, the total number of function calls will be $2 \\times \\text{maxiter}$. The `maxiter` can be increased to any higher value for better energy estimation." ] }, { @@ -404,8 +394,7 @@ "### Step 4: Post-process and return result in desired classical format\n", "\n", "* Input: Ground-state energy estimates during optimization\n", - "* Output: Estimated ground-state energy\n", - "\n" + "* Output: Estimated ground-state energy" ] }, { @@ -434,9 +423,8 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "
" + "\"Output" ] }, "metadata": {}, @@ -456,7 +444,7 @@ "id": "a3f4171f", "metadata": {}, "source": [ - "## Large-scale hardware example\n" + "## Large-scale hardware example" ] }, { @@ -464,7 +452,7 @@ "id": "746e6133", "metadata": {}, "source": [ - "A large-scale hardware example is not included in this tutorial. As the number of qubits increases, VQE encounters significant challenges due to the [barren plateau](https://quantum.cloud.ibm.com/learning/en/courses/variational-algorithm-design/optimization-loops#barren-plateaus) phenomenon: the gradient of the cost function vanishes exponentially with system size, making optimization practically infeasible for large circuits. Combined with hardware noise, this means that scaling VQE to larger spin chains does not produce reliably reproducible results. For approaches that overcome these limitations, see the Next Steps section below.\n" + "A large-scale hardware example is not included in this tutorial. As the number of qubits increases, VQE encounters significant challenges due to the [barren plateau](https://quantum.cloud.ibm.com/learning/en/courses/variational-algorithm-design/optimization-loops#barren-plateaus) phenomenon: the gradient of the cost function vanishes exponentially with system size, making optimization practically infeasible for large circuits. Combined with hardware noise, this means that scaling VQE to larger spin chains does not produce reliably reproducible results. For approaches that overcome these limitations, see the Next Steps section below." ] }, { @@ -477,8 +465,7 @@ "\\[1] Spall, J. C. (2002). Implementation of the simultaneous perturbation algorithm for stochastic optimization.\n", "IEEE Transactions on Aerospace and Electronic Systems, 34(3), 817-823.\n", "\n", - "\\[2] Sahin, M. Emre, et al. (2025). Qiskit Machine Learning: an open-source library for quantum machine learning tasks at scale on quantum hardware and classical simulators. arXiv:2505.17756.\n", - "\n" + "\\[2] Sahin, M. Emre, et al. (2025). Qiskit Machine Learning: an open-source library for quantum machine learning tasks at scale on quantum hardware and classical simulators. arXiv:2505.17756." ] }, { @@ -501,7 +488,7 @@ "\n", "1. **Experiment with ansatz depth:** Modify the `reps` parameter in `efficient_su2` (e.g., try `reps=1` and `reps=3`). How does ansatz depth affect the estimated ground-state energy and convergence speed? At what point do you observe diminishing returns or instability?\n", "2. **Tune SPSA hyperparameters:** Adjust the learning rate schedule parameters (`a`, `c`, `alpha`, `gamma`, `A`) and observe how they impact convergence. Can you find a configuration that converges faster than the defaults used here?\n", - "3. **Compare coupling topologies:** Instead of using the backend's native coupling map, try constructing a simple nearest-neighbor linear chain and compare the results. How does the connectivity of the physical hardware affect the transpiled circuit depth and final energy estimate?\n" + "3. **Compare coupling topologies:** Instead of using the backend's native coupling map, try constructing a simple nearest-neighbor linear chain and compare the results. How does the connectivity of the physical hardware affect the transpiled circuit depth and final energy estimate?" ] }, { @@ -515,7 +502,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -529,7 +516,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.10" + "version": "3" } }, "nbformat": 4, diff --git a/public/docs/images/tutorials/spin-chain-vqe/extracted-outputs/7e8d2f10-f1d6-4ec2-bac9-9db23499c9e1-0.avif b/public/docs/images/tutorials/spin-chain-vqe/extracted-outputs/7e8d2f10-f1d6-4ec2-bac9-9db23499c9e1-0.avif index 55eb9e2315b9a95c2c7944cc1097ae0b6e162cfa..3659d935da1b5cd8c7b3a62ac628b2cd00a4f371 100644 GIT binary patch delta 11809 zcmV++F5c1aRj+6m0000SW^{RQVRmU|00000ZE0pPVRmU|kr-bJGG+h*00000Gy1WC z4FMN2qitkibP@mx3K?o;wPC>l4F-fV)nv1!0pJ9GF7)92XD}R$u#^g~Z%G1Z(W7E0 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docs/tutorials/spin-chain-vqe.ipynb | 26 +++++++++----------------- 1 file changed, 9 insertions(+), 17 deletions(-) diff --git a/docs/tutorials/spin-chain-vqe.ipynb b/docs/tutorials/spin-chain-vqe.ipynb index ceed5a16934..6d019d67ed4 100644 --- a/docs/tutorials/spin-chain-vqe.ipynb +++ b/docs/tutorials/spin-chain-vqe.ipynb @@ -28,7 +28,7 @@ "After completing this tutorial, you can expect to understand the following information:\n", "- How to model a Heisenberg spin chain as a quantum Hamiltonian using Qiskit\n", "- How to use the SPSA optimizer to estimate the ground-state energy of a quantum system\n", - "- How to execute variational workflows on IBM quantum hardware using Qiskit Runtime primitives and sessions\n", + "- How to execute variational workflows on IBM® quantum hardware using Qiskit Runtime primitives and sessions\n", "\n", "## Prerequisites\n", "It is recommended that you familiarize yourself with these topics:\n", @@ -40,9 +40,9 @@ "\n", "The Heisenberg spin chain is one of the most widely studied models in condensed matter physics and quantum magnetism. It describes a one-dimensional lattice of interacting quantum spins, where nearest-neighbor spins are coupled through exchange interactions. The Hamiltonian for the isotropic Heisenberg model with an external magnetic field is given by:\n", "\n", - "$$H = \\sum_{\\langle i,j \\rangle} \\left( J_x X_i X_j + J_y Y_i Y_j + J_z Z_i Z_j \\right) + \\sum_{i} h_i Z_i$$\n", + "$$H = \\sum_{\\langle i,j \\rangle} \\left( J_x X_i X_j + J_y Y_i Y_j + J_z Z_i Z_j \\right) + \\sum_{i} h_i Z_i,$$\n", "\n", - "where $X_i$, $Y_i$, and $Z_i$ are the Pauli operators acting on site $i$, the sum $\\langle i,j \\rangle$ runs over nearest-neighbor pairs, $J_x = J_y = J_z = 0.5$ are the exchange coupling constants (isotropic in this tutorial), and $h_i$ represents a site-dependent external magnetic field. In this tutorial, the magnetic field values are randomly sampled from the interval $[-1, 1]$. Note that in the implementation below, the set of \"nearest-neighbor\" pairs is determined by the hardware backend's native coupling among the first $N$ qubits, which may not form a strict linear chain depending on the device topology.\n", + "where $X_i$, $Y_i$, and $Z_i$ are the Pauli operators acting on site $i$, the sum $\\langle i,j \\rangle$ runs over nearest-neighbor pairs, $J_x = J_y = J_z = 0.5$ are the exchange coupling constants (isotropic in this tutorial), and $h_i$ represents a site-dependent external magnetic field. In this tutorial, the magnetic field values are randomly sampled from the interval $[-1, 1]$. Note that in the implementation below, the set of \"nearest-neighbor\" pairs is determined by the hardware backend's native coupling among the first $N$ qubits, which might not form a strict linear chain depending on the device topology.\n", "\n", "Understanding the ground-state energy of this Hamiltonian is of fundamental importance in physics. The ground state encodes information about quantum phase transitions, entanglement structure, and magnetic ordering. Classically, computing the exact ground-state energy becomes intractable as the number of spins grows, since the Hilbert space dimension scales exponentially as $2^N$ for $N$ spins. This makes it a natural candidate for quantum simulation.\n", "\n", @@ -124,7 +124,7 @@ "* Input: Number of spins\n", "* Output: Ansatz and Hamiltonian modeling the Heisenberg chain\n", "\n", - "Construct an ansatz and Hamiltonian that model a 10-spin Heisenberg chain. In this step, we will build a 10-spin Heisenberg Hamiltonian over the least-busy backend's coupling map and prepare the `efficient_su2` ansatz" + "Construct an ansatz and Hamiltonian that model a 10-spin Heisenberg chain. In this step, we will build a 10-spin Heisenberg Hamiltonian over the least-busy backend's coupling map and prepare the `efficient_su2` ansatz." ] }, { @@ -253,7 +253,7 @@ "\n", "In this tutorial, the hyperparameters for the $i^{th}$ iteration, $a_i$ and $c_i$, are computed as\n", "\n", - "$$a_i = \\frac{a}{(A + i + 1)^\\alpha} \\quad \\text{and} \\quad c_i = \\frac{c}{(i+1)^\\gamma}$$\n", + "$$a_i = \\frac{a}{(A + i + 1)^\\alpha} \\quad \\text{and} \\quad c_i = \\frac{c}{(i+1)^\\gamma},$$\n", "\n", "where the constant values are taken as $A = 30$, $\\alpha = 0.9$, $a = 0.3$, $c = 0.1$, and $\\gamma = 0.4$. These values are selected from [\\[2\\]](#references). Appropriate tuning of hyperparameters is necessary for extracting a good performance out of SPSA." ] @@ -452,7 +452,7 @@ "id": "746e6133", "metadata": {}, "source": [ - "A large-scale hardware example is not included in this tutorial. As the number of qubits increases, VQE encounters significant challenges due to the [barren plateau](https://quantum.cloud.ibm.com/learning/en/courses/variational-algorithm-design/optimization-loops#barren-plateaus) phenomenon: the gradient of the cost function vanishes exponentially with system size, making optimization practically infeasible for large circuits. Combined with hardware noise, this means that scaling VQE to larger spin chains does not produce reliably reproducible results. For approaches that overcome these limitations, see the Next Steps section below." + "A large-scale hardware example is not included in this tutorial. As the number of qubits increases, VQE encounters significant challenges due to the [barren plateau](/learning/courses/variational-algorithm-design/optimization-loops#barren-plateaus) phenomenon: the gradient of the cost function vanishes exponentially with system size, making optimization practically infeasible for large circuits. Combined with hardware noise, this means that scaling VQE to larger spin chains does not produce reliably reproducible results. For approaches that overcome these limitations, see the Next Steps section below." ] }, { @@ -478,26 +478,18 @@ "\n", " If you found this work interesting, you might be interested in the following material:\n", "\n", - " * **Try Sample-based Quantum Diagonalization (SQD):** As demonstrated in this tutorial, VQE faces challenges at scale due to barren plateaus and high measurement overhead. IBM has developed [Sample-based Quantum Diagonalization (SQD)](https://www.ibm.com/quantum/blog/quantum-diagonalization) as a more scalable alternative. Unlike VQE, SQD avoids variational optimization entirely — instead, a quantum computer generates samples and a classical computer projects the Hamiltonian onto a subspace spanned by those samples and diagonalizes it. This provides an upper bound to the ground-state energy with significantly fewer measurements and without susceptibility to barren plateaus. Follow the [SQD tutorial](/docs/tutorials/sample-based-quantum-diagonalization) to see this approach in action.\n", - " * **Explore the Quantum Diagonalization Algorithms course:** Deepen your understanding of both VQE and SQD, including their trade-offs, in the [Quantum Diagonalization Algorithms](/learning/courses/quantum-diagonalization-algorithms) course on IBM Quantum Learning.\n", + " * **Try Sample-based Quantum Diagonalization (SQD):** As demonstrated in this tutorial, VQE faces challenges at scale due to barren plateaus and high measurement overhead. IBM has developed [Sample-based Quantum Diagonalization (SQD)](https://www.ibm.com/quantum/blog/quantum-diagonalization) as a more scalable alternative. Unlike VQE, SQD avoids variational optimization entirely; instead, a quantum computer generates samples and a classical computer projects the Hamiltonian onto a subspace spanned by those samples and diagonalizes it. This provides an upper bound to the ground-state energy with significantly fewer measurements and without susceptibility to barren plateaus. Follow the [SQD tutorial](/docs/tutorials/sample-based-quantum-diagonalization) to see this approach in action.\n", + " * **Explore the Quantum Diagonalization Algorithms course:** Deepen your understanding of both VQE and SQD, including their trade-offs, in the [Quantum diagonalization algorithms](/learning/courses/quantum-diagonalization-algorithms) course on IBM Quantum Learning.\n", "\n", "\n", "### Challenge\n", "\n", "Now that you have a working VQE implementation for the Heisenberg chain, try the following:\n", "\n", - "1. **Experiment with ansatz depth:** Modify the `reps` parameter in `efficient_su2` (e.g., try `reps=1` and `reps=3`). How does ansatz depth affect the estimated ground-state energy and convergence speed? At what point do you observe diminishing returns or instability?\n", + "1. **Experiment with ansatz depth:** Modify the `reps` parameter in `efficient_su2` (for example, try `reps=1` and `reps=3`). How does ansatz depth affect the estimated ground-state energy and convergence speed? At what point do you observe diminishing returns or instability?\n", "2. **Tune SPSA hyperparameters:** Adjust the learning rate schedule parameters (`a`, `c`, `alpha`, `gamma`, `A`) and observe how they impact convergence. Can you find a configuration that converges faster than the defaults used here?\n", "3. **Compare coupling topologies:** Instead of using the backend's native coupling map, try constructing a simple nearest-neighbor linear chain and compare the results. How does the connectivity of the physical hardware affect the transpiled circuit depth and final energy estimate?" ] - }, - { - "cell_type": "markdown", - "id": "a1b8767d", - "metadata": {}, - "source": [ - "© IBM Corp., 2017-2026" - ] } ], "metadata": { From 635a0c28b021be0a764e10945933f4ad753ce583 Mon Sep 17 00:00:00 2001 From: Henry Zou Date: Tue, 12 May 2026 18:52:20 -0400 Subject: [PATCH 4/4] Changed header for Challenges to be ## and move it to be before references --- docs/tutorials/spin-chain-vqe.ipynb | 26 ++++++++++++++++---------- 1 file changed, 16 insertions(+), 10 deletions(-) diff --git a/docs/tutorials/spin-chain-vqe.ipynb b/docs/tutorials/spin-chain-vqe.ipynb index 6d019d67ed4..6270f9df967 100644 --- a/docs/tutorials/spin-chain-vqe.ipynb +++ b/docs/tutorials/spin-chain-vqe.ipynb @@ -455,6 +455,20 @@ "A large-scale hardware example is not included in this tutorial. As the number of qubits increases, VQE encounters significant challenges due to the [barren plateau](/learning/courses/variational-algorithm-design/optimization-loops#barren-plateaus) phenomenon: the gradient of the cost function vanishes exponentially with system size, making optimization practically infeasible for large circuits. Combined with hardware noise, this means that scaling VQE to larger spin chains does not produce reliably reproducible results. For approaches that overcome these limitations, see the Next Steps section below." ] }, + { + "cell_type": "markdown", + "id": "8f14492c", + "metadata": {}, + "source": [ + "## Challenge\n", + "\n", + "Now that you have a working VQE implementation for the Heisenberg chain, try the following:\n", + "\n", + "1. **Experiment with ansatz depth:** Modify the `reps` parameter in `efficient_su2` (for example, try `reps=1` and `reps=3`). How does ansatz depth affect the estimated ground-state energy and convergence speed? At what point do you observe diminishing returns or instability?\n", + "2. **Tune SPSA hyperparameters:** Adjust the learning rate schedule parameters (`a`, `c`, `alpha`, `gamma`, `A`) and observe how they impact convergence. Can you find a configuration that converges faster than the defaults used here?\n", + "3. **Compare coupling topologies:** Instead of using the backend's native coupling map, try constructing a simple nearest-neighbor linear chain and compare the results. How does the connectivity of the physical hardware affect the transpiled circuit depth and final energy estimate?" + ] + }, { "cell_type": "markdown", "id": "217a9379", @@ -470,7 +484,7 @@ }, { "cell_type": "markdown", - "id": "8f14492c", + "id": "8aefe54c", "metadata": {}, "source": [ "## Next steps\n", @@ -480,15 +494,7 @@ "\n", " * **Try Sample-based Quantum Diagonalization (SQD):** As demonstrated in this tutorial, VQE faces challenges at scale due to barren plateaus and high measurement overhead. IBM has developed [Sample-based Quantum Diagonalization (SQD)](https://www.ibm.com/quantum/blog/quantum-diagonalization) as a more scalable alternative. Unlike VQE, SQD avoids variational optimization entirely; instead, a quantum computer generates samples and a classical computer projects the Hamiltonian onto a subspace spanned by those samples and diagonalizes it. This provides an upper bound to the ground-state energy with significantly fewer measurements and without susceptibility to barren plateaus. Follow the [SQD tutorial](/docs/tutorials/sample-based-quantum-diagonalization) to see this approach in action.\n", " * **Explore the Quantum Diagonalization Algorithms course:** Deepen your understanding of both VQE and SQD, including their trade-offs, in the [Quantum diagonalization algorithms](/learning/courses/quantum-diagonalization-algorithms) course on IBM Quantum Learning.\n", - "\n", - "\n", - "### Challenge\n", - "\n", - "Now that you have a working VQE implementation for the Heisenberg chain, try the following:\n", - "\n", - "1. **Experiment with ansatz depth:** Modify the `reps` parameter in `efficient_su2` (for example, try `reps=1` and `reps=3`). How does ansatz depth affect the estimated ground-state energy and convergence speed? At what point do you observe diminishing returns or instability?\n", - "2. **Tune SPSA hyperparameters:** Adjust the learning rate schedule parameters (`a`, `c`, `alpha`, `gamma`, `A`) and observe how they impact convergence. Can you find a configuration that converges faster than the defaults used here?\n", - "3. **Compare coupling topologies:** Instead of using the backend's native coupling map, try constructing a simple nearest-neighbor linear chain and compare the results. How does the connectivity of the physical hardware affect the transpiled circuit depth and final energy estimate?" + "" ] } ],