diff --git a/.github/workflows/unit-tests.yml b/.github/workflows/unit-tests.yml deleted file mode 100644 index bbdcd7e..0000000 --- a/.github/workflows/unit-tests.yml +++ /dev/null @@ -1,40 +0,0 @@ -name: Unit tests - -on: - push: - # Trigger on all pushes to main and dev branches regardless of the files changed (failsafe) - branches: - - main - - dev - pull_request: - paths: - - ".github/workflows/unit-tests.yml" # Trigger on changes to this workflow file - - "src/**" - - "tests/**" - - "*.toml" - - "uv.lock" - -jobs: - unit-tests: - name: Run unit tests - runs-on: ubuntu-latest - - steps: - - name: Checkout code - uses: actions/checkout@v4 - - - name: Setup Python - uses: actions/setup-python@v5 - with: - python-version: 3.12 - - - name: Install uv - uses: astral-sh/setup-uv@08807647e7069bb48b6ef5acd8ec9567f424441b - with: - enable-cache: true - - - name: Install dependencies - run: uv sync --locked --group dev - - - name: Run unit tests with pytest - run: uv run pytest tests/ diff --git a/challenges/Qiskit-ibm-runtime/ibm_runtime_challenge.md b/challenges/Qiskit-ibm-runtime/ibm_runtime_challenge.md new file mode 100644 index 0000000..12baac3 --- /dev/null +++ b/challenges/Qiskit-ibm-runtime/ibm_runtime_challenge.md @@ -0,0 +1,21 @@ +# Défi - Exécuter un programme sur IBM Quantum Platform + +## Objectif + +L'objectif de ce défi est d'exécuter un programme quantique sur la plateforme IBM Quantum en utilisant les librairies `qiskit` et `qiskit-ibm-runtime`. Il sera demandé d'exécuter un circuit quantique préconçu sur un simulateur bruité configuré à partir d'un modèle de bruit, puis de le faire tourner sur une véritable machine. + +Vous devrez comparer les résultats obtenus sur le simulateur et la machine réelle, et expliquer les différences observées. + +Une étape bonus consistera à appliquer des techniques de mitigation du bruit pour améliorer les résultats obtenus sur la machine réelle, et à comparer les résultats avant et après mitigation. + +## Données fournies + +Un circuit quantique préconçu se trouve dans le dossier `notebooks`, accompagné d'exemples de code qui devront être complétés pour exécuter le circuit sur les différentes plateformes. + +## Résultats à produire + +1. (Optionel) L'histogramme des résultats obtenus à partir du simulateur idéal (sans bruit). +2. L'histogramme des résultats obtenus à partir du simulateur bruité. +3. L'histogramme des résultats obtenus à partir de la machine réelle. +4. (Optionel) Une analyse textuelle comparative des résultats obtenus avec les différentes plateformes, en mettant en évidence les différences et en formulant une hypothèse sur les causes de ces différences (par exemple, les erreurs de mesure, les erreurs de porte, etc.). +5. (Optionel) L'application de techniques de mitigation du bruit pour améliorer les résultats obtenus sur la machine réelle, et une comparaison des résultats avant et après mitigation à l'aide d'histogrammes. diff --git a/challenges/Qiskit-ibm-runtime/pyproject.toml b/challenges/Qiskit-ibm-runtime/pyproject.toml new file mode 100644 index 0000000..2980cff --- /dev/null +++ b/challenges/Qiskit-ibm-runtime/pyproject.toml @@ -0,0 +1,10 @@ +[project] +name = "ibm-runtime" +version = "0.1.0" +requires-python = ">=3.12" +dependencies = [ + "matplotlib>=3.10.9", + "qiskit>=2.4.1", + "qiskit-aer>=0.17.2", + "qiskit-ibm-runtime>=0.47.0", +] diff --git a/notebooks/ibm-runtime.ipynb b/notebooks/ibm-runtime.ipynb new file mode 100644 index 0000000..1efb889 --- /dev/null +++ b/notebooks/ibm-runtime.ipynb @@ -0,0 +1,493 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "cece969d", + "metadata": {}, + "source": [ + "### Installation des dépendances\n", + "\n", + "Exécutez la commande suivante pour installer les dépendances à partir de la racine du repo:\n", + "\n", + "```bash\n", + "uv sync --package ibm-runtime\n", + "```\n", + "\n", + "Un environnement virtuel devrait être créé, il pourra être utilisé avec ce notebook." + ] + }, + { + "cell_type": "markdown", + "id": "54026602", + "metadata": {}, + "source": [ + "### Références\n", + "\n", + "Ce notebook est inspiré de la documentation sur la mitigation d'erreur, remplaçant la collecte des résultats par *expectation value* en *samples* : https://quantum.cloud.ibm.com/docs/en/tutorials/combine-error-mitigation-techniques\n", + "\n", + "La section sur la simulation bruitée est tirée de la documentation de Qiskit Aer : https://qiskit.github.io/qiskit-aer/tutorials/2_device_noise_simulation.html" + ] + }, + { + "cell_type": "markdown", + "id": "02ff4619", + "metadata": {}, + "source": [ + "## 1. Construction d'un circuit quantique\n", + "\n", + "Voici ci-dessous un circuit quantique à 10 qubits servant d'exmple. Construit à partir d'une structure de type `EfficientSU2`, ce gabarit est parfois utilisé en optimisation ou en chimie. Vous pouvez trouver plus d'informations sur ce circuit dans la [documentation de Qiskit - EfficientSU2](https://qiskit.org/documentation/stubs/qiskit.circuit.library.EfficientSU2.html).\n", + "Malgré son implémentation optimisée pour l'efficacité sur les appareils quantiques, on peut constater que, pour une seule occurrence du motif, le circuit contient un nombre de portes élémentaires relativement important." + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "id": "ae6845cb", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "from qiskit.circuit.library import efficient_su2, unitary_overlap\n", + "from qiskit.quantum_info import SparsePauliOp" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "id": "9de815b3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "execution_count": 150, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n_qubits = 10 # Nombre de qubits du circuit\n", + "reps = 1 # Nombre de répétitions du motif\n", + "\n", + "circuit = efficient_su2(n_qubits, entanglement=\"pairwise\", reps=reps)\n", + "\n", + "circuit.decompose().draw(\"mpl\", scale=0.7) # Circuit décomposé pour mieux voir les portes élémentaires" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e4b04d55", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "c59881c5", + "metadata": {}, + "source": [ + "Une technique courante pour valider un résultat d'expérience, particulièrement dans le cas d'un circuit de grande taille, est d'utiliser un circuit miroir, ou *mirror circuit*. Cette technique, également appelée *compute-uncompute*, consiste à exécuter le circuit dans un ordre inverse après l'avoir exécuté dans l'ordre normal. En théorie, si le circuit est parfaitement implémenté et que les portes sont idéales, le circuit miroir devrait ramener l'état quantique à son état initial, ce qui permet de vérifier que les opérations ont été correctement appliquées.\n", + "\n", + "Le circuit précédent peut prendre des paramètres d'entrée, que nous appliquerons aux deux versions du circuit (normal et miroir).\n", + "\n", + "Vous utiliserez donc la variable `mirror_circuit` dans la suite du notebook pour tester vos exécutions." + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "id": "8c68d3e5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 151, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Génération de paramètres aléatoires pour le circuit\n", + "rng = np.random.default_rng(67)\n", + "params = rng.uniform(-np.pi, np.pi, size=circuit.num_parameters)\n", + "\n", + "# Assignation des paramètres au circuit\n", + "assigned_circuit = circuit.assign_parameters(params)\n", + "\n", + "# Construction du circuit miroir, en insérant une barrière pour mettre en évidence l'axe de symétrie\n", + "mirror_circuit = unitary_overlap(assigned_circuit, assigned_circuit, insert_barrier=True)\n", + "\n", + "mirror_circuit.decompose().draw(\"mpl\", scale=0.7)" + ] + }, + { + "cell_type": "markdown", + "id": "47b26cfa", + "metadata": {}, + "source": [ + "Nous allons maintenant ajouter la mesure en base Z à la fin du circuit miroir pour pouvoir récupérer les résultats de l'exécution. Ce serait un bon moment pour émettre une hypothèse sur ce que vous vous attendez à voir comme résultat de l'exécution du circuit miroir." + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "id": "f9d695a3", + "metadata": {}, + "outputs": [], + "source": [ + "mirror_circuit.measure_all()" + ] + }, + { + "cell_type": "markdown", + "id": "bfb0e0f8", + "metadata": {}, + "source": [ + "## 2. Exécution sur simulateur local\n", + "\n", + "La prochaine section traite de l'exécution du circuit précédent sur un simulateur local en utilisant la bibliothèque `qiskit-aer` dont la documentation se trouve sur [Qiskit Aer](https://qiskit.github.io/qiskit-aer/).\n", + "Une documentation concernant la simulation de bruit est également dispobnible sur [Device backend noise model simulations](https://qiskit.github.io/qiskit-aer/tutorials/2_device_noise_simulation.html#).\n" + ] + }, + { + "cell_type": "markdown", + "id": "f1e9b63e", + "metadata": {}, + "source": [ + "### Définition du simulateur local et configuration du bruit" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "id": "e305d97f", + "metadata": {}, + "outputs": [], + "source": [ + "from qiskit_aer.primitives import SamplerV2\n", + "from qiskit_ibm_runtime.fake_provider import FakeMarrakesh # Modèle de bruit du même nom qu'un ordinateur quantique d'IBM, vous pouvez le changer pour le même modèle qui sera retournée par `.least_busy()` plus loin\n", + "from qiskit_ibm_runtime import SamplerOptions\n", + "from qiskit import transpile\n", + "from qiskit.transpiler import PassManager\n", + "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "id": "da536473", + "metadata": {}, + "outputs": [], + "source": [ + "# Définition du simulateur local configuré avec le modèle de bruit d'une machine réelle d'IBM\n", + "backend = FakeMarrakesh()\n", + "\n", + "simulator = SamplerV2.from_backend(backend)" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "id": "a4076a51", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "execution_count": 155, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Transpilation du circuit pour le simulateur\n", + "pass_manager : PassManager = generate_preset_pass_manager(\n", + " optimization_level=3, backend=backend, seed_transpiler=67\n", + ")\n", + "\n", + "isa_circuit = pass_manager.run(mirror_circuit)\n", + "\n", + "isa_circuit.decompose().draw(\"mpl\", scale=0.7, idle_wires=False, fold = -1)" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "id": "05f77855", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 156, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from qiskit.visualization import plot_histogram\n", + "\n", + "# Exécution du circuit sur le simulateur\n", + "result = simulator.run(pubs=[(isa_circuit)], shots=1000).result()\n", + "\n", + "counts = result[0].data.meas.get_counts()\n", + "\n", + "plot_histogram(counts, title=\"Résultats de l'exécution du circuit miroir sur simulateur bruité\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "88121a7b", + "metadata": {}, + "source": [ + "## 3. Exécution sur un ordinateur quantique réel\n", + "\n", + "Dans cette section, vous allez exécuter le même circuit sur un ordinateur quantique réel d'IBM en utilisant `qiskit-ibm-runtime` et vos informations de compte de la plateforme IBM Quantum.\n", + "\n", + "Le club a déjà traité de la configuration dans [ce notebook sur Deepnote](https://deepnote.com/workspace/QuantumETS-bd6b11e4-0d15-4576-90e5-88412c5f674e/project/7-Executer-du-code-sur-un-ordinateur-quantique-86da04dd-0f4f-44c4-a6dc-3bf69b0f57fd/notebook/Notebook-1-b43399ed06384c5a95ac0f2afeca9f25?utm_source=share-modal&utm_medium=product-shared-content&utm_campaign=notebook&utm_content=86da04dd-0f4f-44c4-a6dc-3bf69b0f57fd)." + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "id": "fdb1fec4", + "metadata": {}, + "outputs": [], + "source": [ + "from qiskit_ibm_runtime import QiskitRuntimeService, Sampler\n", + "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n", + "from qiskit.transpiler import PassManager" + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "id": "0cf64b02", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "qiskit_runtime_service._discover_account:WARNING:2026-05-29 16:34:50,260: Loading account with the given token. A saved account will not be used.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Backend choisi : \n" + ] + } + ], + "source": [ + "# Il existe une façon de sauvegarder le compte https://quantum.cloud.ibm.com/docs/en/api/qiskit-ibm-runtime/qiskit-runtime-service#save_account\n", + "\n", + "service = QiskitRuntimeService(channel=\"ibm_cloud\", token=\"jaTUi37yoJ73TsR5oN4vPAOWfXp8vgdxL2tG7NuNio_g\", instance=\"Deepnote\")\n", + "backend = service.least_busy(operational=True, simulator=False)\n", + "print(f\"Backend choisi : {backend}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "id": "12f09eea", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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frK/vvvtu+znrNdZr77nnHo0dO7a42N5a4b5FixZO/zmuFxUOq98OHjzYLrAfNWqU2rZtq7fffluDBg2yn+/evbtM1fiK0Wo4aJjcLHvTcm184x7tXzpH/txsRac0UM0e56nZtc/azzcdOta+PNaWd0ep5W2vO91cY3klzm7Paa/E2e2IMyoy4f1lunH0HB26mP2yNXs06Pav9caYU3X1ee5dJcTtfZ2F7ZA4eymf3e5fH6QG5T0H9moS8Pf1Gvo64uyl/o58Js5eyufyWCuumMKEONN3EOfKIp/htX7DhJx2s/enr1H6/j9WSwuEqV+v1Y7d2apbKy6g7+s1XtkGTeCFWJvS181fssMusM/OKSjx+M49ORrxz3lasT5drz7S2zVXt/VCbpjAC3E2YRv0QpxNQJzh5fkvAICzVq9erTFjxigzM1MdOnTQDTfcoMaNGx/2upSUFB133HH2bfPmzXYB/vLly7V9+3YlJCRQbF8Fni+4f+WVV+wzMhYuXKguXbrYj/Xt21etWrVS7dq11bx5c6eb6GkrR5+jlF4XqtUd/1FEbIJytq5SxtI5TjfLc4gzcfYS8hnl2bknW7c+Mdf+uvCQhW2Kvv/bmDm6oH8LJSdxqSO2Q3ejv0NOboF+Wbwj4IGYs3AbwWUbdA36OuLsJeSz82699Vanm+Ap5DRx9hLymTgjdOYsCPwxZ35BoX76PU1n924W8Pf2Evo6Yu1FN4yeYxfbF5Sxkv3rH63QVX9qq/49Dy8acQLbIXH2EvKZOMOdmP8CAFRGRkaGnnrqKbvY3qqHvv766xUREVHhzzVs2FB169a1C+4tkZGRqlOnDkGvJM8X3D/55JMaMmRIcbF9UZJYBffR0dH299YlFD744AOtXbtWixYtUteuXUt9r4cfftheJb+81+AP+ek7lLNlpeqdeaMi45Psx+Iat7NvCBziHBrEmTjDHazJ9dw8f7mvycwu0H//t0p/u7RTyNrlJfR3xBmhs3jlbuXll9+nVcfm7ZnatjNLDerEB/y9vYK+jjh7CflMnMOJdcXKqVOnOt0MT6DvIM5eQj4TZ4TW/KU7g/S+Oyi4Lwd9XegQ69BZsHRHhYtRREb49OKkpa4ouCc3iLOXkM/EGe7F/BcAoDJef/117d69W927d690sb3f79cLL7yg77//XklJSWratKmWLVtmv9fNN99M4MO94H7FihVas2aNxo8ff9hzGzZs0EUXXWR/fcEFF+i2225T7969y3yv+fPn64cfflCLFi0q/ftr1Kih3NzcCl+X0KGX2oyZJSf069dXmakHVgquisq0OSq5ruKadtLa8X9RvTNuUGK7ExTbqI2caveJKbX1Vc9Tq/Qzo5Yt1uPLl5Z4bG9+9S6V2q9fP/2wZ5fn4+xkPgczp90WZ6dzujr5bGqc4S75Da+SkntIvsiyX1RYoJuHPaHbr343qG1h/1019HWh6e+CObYLFhNz40j2hQfzJ3SUmpVcqTd97lWKiT68j4uJPnCAnP3L0MOey80rUHKvt0o81qxlR/lyt+pIVSfOTo85TBxDWxjbhSbOJvZ3Jo6hyefQxNnEfC7P0KGH7+MOtXXrVk2cOLHM50855RQFmolxdnJfSL8RmjiH07ydiWM7xtChibOpfbSJ46RD5bYeLUXXCfhx7EOPPKXRt3ygQDAxzoz73RFnN46TTOzrKqvAmutvVP5xgLXy/Qef/aTYF85WMJmYGybOj5o4tmMMHZo4mziGNnX/DedVNAdW0fxXsObAquOWB5+172NjY0t87Wa0mTiTG2yDpvUb7dq108iRI0s8tnHjRn333XdKSEjQX//61yoV28+ZM8eua37ggQfse2uxcuu9rBpqqwC/tHGSVYftNTExMdq3b1+Vf87TBfebNm2y7+vXr1/iceusjHXr1tlnd1RmIJKTk6ObbrpJ77zzjn35BTfo8KhzH4xURftHZ2nbh09r6+THlLVhiWLqNVeTq59Q7VMuKX5NREyc4pp0lBuN6thFd7Yp2bZ6n7tvJTXT42xKTnshzibktBfijCAqzK/Ei3yVfF3omdDXeWE7NKGv80KcTclnd+dG4Fe3L1YYxPc2Mtbe2wYtxDk0TOjvyGfi7KV89gJT4mx632HCftALcSafQ8eEnDY9n03KaVfnRtCONwvlJNfF2aPboAlx9kKsjenrCgsC+7oQMD032AZDw5Rt0PR8NiWnvRBnAADgLl9++aV9f9ppp6l27drVKrYvWnjceo9PPvlE06dP11/+8pegt910ni64r1PnwAobK1euVI8ePeyvCwsLde+999pJVFRwX5EHH3xQQ4YMUcuWLav0+yt7BsTCndJ1c+SImTNnqdsfC5FUWmXbHJ1SX02HjpWGjlVBZrrSvnhFa565Qgktj1Zc0wMHDNG1GqrhxfcFvd3+JctUcN9DcsrMmTMV0bmj5+PsZD4HO6fdFGenc7o6+WxqnOEuH85Yq0G3f13+i3wR+vy9J3X6Sf8NalvYf1dtO6SvC01/F+yxXTCYmBtHsi882OqN6Wpz9vslHjt0hb8iRSsCxh3/eoXvGxHh0560NUqIP/LDPRPjbOIY2sLYLjRxNrG/M3EMTT6Tz9Xx888/V/gaaxGN6667rsznJ0yYoEDzar/htvGoV/sNt/XRJs7bmRhnxtChG3OY2Eeb2N8dqv+1n2nmz1sCehxrGffUSN18+TsKBBPjzLjfPXF22zjJxL6usrbvzFLTge8oL7/sE258Pumev52hJ25/IHgNMTQ3GHMwhi4LY+iqC6f9N5xX0RxYRfNfwZoDq44RT08sXsj24K/djDYTZ3KDbdC0fiMtLU0zZswo8dj8+fOLV58/kmJ7S//+/e2C+wULFpQ5TqpXr94R/x1eUfG1BAzWqVMntW3bVsOHD9f777+vzz//XOeff76dcNblFDp06FDhe8ydO1e//PKL/v73v4ekzV4WmZCshoOGKTKxprLWL3a6OZ5FnImzl5DPONSfTm2ulo2TFBnhKz1nInzq2KqmBpzYhOCxHRqF/i48tWpSQ7WSYwL+vp1a1QxIsX04YRskzl5CPhNnL3v66aedboJn0XcQZy8hn4kzguu4znWD9L5UjFUFfV3oEOvgqV8nXkP+1NYuqi+N9Xh0ZIRuuNidqz6TG8TZS8hn4gz3YP4LAFDRIuBWEX5ycrIaNWp0RMX2loYNG9rPWe9Z2QXGw5mnC+6joqI0efJkO7GuueYa3X777TrrrLPUp08fHX300YqIqPjP/+abb7R06VK1atXKXuF+48aNOuOMM+xLKKB8+Rm7temt4XZxfWF+nvx5OUqbNkH+nEwltDmO8AUIcQ4N4kyc4Q5RURH63wunq05KrEqbg29QJ16f/PN0e3VnVA/9XWgQZ1h8Pp/OPLlpwINxdu9mBJht0BXo64izl5DP7nDbbbc53QTPIKeJs5eQz8QZoXV278Afx9avHRe0Qn6voK8j1l41/r5eOumY+vbXB8/rW19HRUbovWf6q1XTGnIDtkPi7CXkM3GGezH/BQAoz/bt2+37Jk2a2PUGR1Jsb7Heo2nTpiXeG2Xz/LKHxxxzjL1K/cGef/55u+i+Mu677z77VsQquv/000/VtWvXgLfVa3xRMcrbvVUrHz1P+Xu2yRcdq/hmXdR2xMeKbdBSbvfVSaVfciPtrEFyE9PjbAovxNmEnPZCnBF8ndvU0m8fXKgJ7y/TQy8euExSm6Y19JdB7XX9xR1Vt1Yc/4Yw3g5N6Ou8EGcTuTU3/n5pJ73z+eqAvZ91TH3jJc6u+uXWWHttGyTOKEI+h4YX4uwF8+bNc7oJnmF6TpuwH/RCnE3hhTibkNNeiLOJ3JgbfU9opE6tU7R09Z6Aved1F3ZQTHSknOLGOHtxGzQhzl6JtUmSEqL19cSz9d//rdKLk5Zo3pKd9uM3Du6omy/vbPc3bmF6brANwkv5bEpOeyHOCD3mvwAA5bEK7ceMGaOYmJhyX2etWP/bb7+VW2xf5M9//rNyc3Pt90aYF9wfKisrS6mpqfZq90VuvfVWTZkyRVu3btWAAQNUp04dLV682NF2ekFkXKJa3vqq083wPNPinLHkO218bZjki1Ctky5SgwvuKn4udYR1UFxof938hhcV37yztkwarfSFXyqmQSu1vOXf8kU6022ZFmdTEWdUlrWS/YM3dtej/1pof7/ys0tcE7z1r9yirDULlXzsWWo0eHjx41bft3PGG2p67T9Up+8Q+7GVY85VQWa63be1vnuSopLrOt7vsR0SZ4TWyd0b6PSTmmj695sC8n5/vqC9WjdNDsh7eRl9HXH2EvKZOJclf98urXhooLI3par7pIzDnvfnZmvR9a3U5p73ldT5FK17/q/K3rxchf4CtbzlVcU1aR+i6JqtvDivfuoy5e3ZKn/WPkXXbaa2wz8s9bjACfQdxNlLyGfijNCyVj575O/HavCwGQF5vzo1Y3XblV0C8l5eRl9HrL0sNibSXlDHusUe95r92AsjTpLbsB0S58p+HlTaZ96Lb+qkqJQG9mNt7puiqBq1tefHj7X9k+dUWOhXq9vfVEy95uSzx9BvAACAQIuLi1Pbtm0rfF2DBg3sQntLecX2lubNQzcONV3YFdxbZ20UFBSoe/fuxY+NHz/evlXG2rVrg9g6wPtiGrRW+8dmKyI6xp5sqHfW3xQRm2A/54uIVPvRXxW/Nm/XFmWuXqAOj8/W9v89rz0/faJavdxzRjoAHGr/il/ki4hSh8e/1aonLlbenm2K/v8J1AaD7lZc85JXyLEmVX1R0do58y3t+uZt1Tp5MP0ejJe1fol2fz9Z6fM+K/UEOxxeqPCvh05R1wunaN/+vDLDk5tXUGHomtRP0DN39SDEQJBPFC6tuLYwP09rnr1SeXu3q+Zx56jhhXfzf4DjIuNrqN3DX2r12NJPTt359WuKb9a5+PvmN75oj033Lf5WadNeVrNrn5XbuHF1lfLi3Prud+37tGkTDlyGpozjAsCk4qHS9nk529Z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+ "text/plain": [ + "
" + ] + }, + "execution_count": 159, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pass_manager : PassManager = generate_preset_pass_manager(\n", + " optimization_level=3, backend=backend, seed_transpiler=67\n", + ")\n", + "\n", + "isa_circuit = pass_manager.run(mirror_circuit)\n", + "\n", + "isa_circuit.decompose().draw(\"mpl\", scale=0.7, idle_wires=False, fold = -1)" + ] + }, + { + "cell_type": "markdown", + "id": "8cdc4030", + "metadata": {}, + "source": [ + "### Exécution sans mitigation d'erreur\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "id": "e5e096b2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 160, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sampler = Sampler(mode=backend)\n", + "\n", + "results = sampler.run([isa_circuit], shots=1000).result()\n", + "\n", + "counts = results[0].data.meas.get_counts()\n", + "\n", + "counts_0x000 = [counts.get(\"0000000000\")]\n", + "\n", + "plot_histogram(counts, title=\"Résultats de l'exécution du circuit miroir sur machine réelle\")" + ] + }, + { + "cell_type": "markdown", + "id": "82d895cb", + "metadata": {}, + "source": [ + "### Exécution avec les techniques de mitigation d'erreur DC et Pauli Twirling" + ] + }, + { + "cell_type": "code", + "execution_count": 161, + "id": "47b76281", + "metadata": {}, + "outputs": [], + "source": [ + "from qiskit_ibm_runtime import Batch\n", + "\n", + "pub = (isa_circuit)\n", + "\n", + "jobs = []\n", + "\n", + "with Batch(backend=backend) as batch:\n", + " sampler = Sampler(mode=batch)\n", + " # Set number of shots\n", + " sampler.options.default_shots = 1000\n", + "\n", + " # Add dynamical decoupling (DD)\n", + " sampler.options.dynamical_decoupling.enable = True\n", + " sampler.options.dynamical_decoupling.sequence_type = \"XpXm\"\n", + " job1 = sampler.run([pub])\n", + " jobs.append(job1)\n", + "\n", + " # Add gate twirling (DD + Gate Twirling)\n", + " sampler.options.twirling.enable_gates = True\n", + " sampler.options.twirling.num_randomizations = \"auto\"\n", + " job2 = sampler.run([pub])\n", + " jobs.append(job2)" + ] + }, + { + "cell_type": "code", + "execution_count": 162, + "id": "13510183", + "metadata": {}, + "outputs": [], + "source": [ + "# Lancement de toutes les jobs en parallèle\n", + "results = [job.result() for job in jobs]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a745f6fb", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Déballage des résultats de chaque job (il n'y a qu'un seul résultat PUB dans chaque résultat de job)\n", + "pub_results = [result[0] for result in results]\n", + "\n", + "# Déballage des comptes pour chaque résultat de PUB\n", + "counts = [pub_result.data.meas.get_counts() for pub_result in pub_results]\n", + "\n", + "counts_0x000 += [count.get(\"0000000000\") for count in counts]\n", + "\n", + "# Tracer le compte pour la bitstring \"000000000\" (correspondant à l'état |000...0⟩), pour chaque technique de mitigation d'erreur\n", + "fig, ax = plt.subplots()\n", + "labels = [\"No mitigation\", \"+ DD\", \"+ Twirling\",]\n", + "ax.bar(\n", + " range(len(labels)),\n", + " counts_0x000,\n", + " label=\"experiment\",\n", + ")\n", + "ax.axhline(y=1000, color=\"gray\", linestyle=\"--\", label=\"ideal\")\n", + "ax.set_xticks(range(len(labels)))\n", + "ax.set_xticklabels(labels)\n", + "ax.set_ylabel(\"Count\")\n", + "ax.legend(loc=\"upper left\")\n", + "\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "iqucodefest-practice (3.14.3)", + "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": "3.14.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/solutions/vqe_qchem/src/ham_sdk_conversion.py b/solutions/vqe_qchem/src/ham_sdk_conversion.py new file mode 100644 index 0000000..eef3e9f --- /dev/null +++ b/solutions/vqe_qchem/src/ham_sdk_conversion.py @@ -0,0 +1,95 @@ +import pennylane as qml +from pennylane.ops.op_math import LinearCombination, Prod +from qiskit import QiskitError +from qiskit.quantum_info import SparsePauliOp +import pprint as pp +import logging + +logger = logging.getLogger(__name__) + +def map_pl_hamiltonian_to_qiskit(hamiltonian: LinearCombination) -> SparsePauliOp: + """Convert a Pennylane Hamiltonian representation to a Qiskit representation. + + The function takes in a Pennylane LinearCOmbination object and construct the corresponding Qiskit SparsePauliOp object. + + the SparsePauliOp class has a method from_sparse_list that can be used to create a SparsePauliOp object from a list of (pauli_string, [indices], coeff) tuples. The pauli_string is a string representation of the Pauli operators (e.g., "XZ"), the indices are the qubits on which the operators act, and coeff is the coefficient of the term in the Hamiltonian. + + Args: + hamiltonian (LinearCombination): A Pennylane Hamiltonian represented as a LinearCombination of operators. + + Returns: + SparsePauliOp: A Qiskit SparsePauliOp object representing the same Hamiltonian. + """ + + coeffs, pauli_ops = hamiltonian.terms() + terms = [] + for pauli_op in pauli_ops: + if isinstance(pauli_op, Prod): + observables = "" + qubits = [] + # Extract the individual operators and their corresponding qubits + for op in pauli_op.decomposition(): + if isinstance(op, qml.X): + observables += "X" + qubits.append(op.wires[0]) + elif isinstance(op, qml.Y): + observables += "Y" + qubits.append(op.wires[0]) + elif isinstance(op, qml.Z): + observables += "Z" + qubits.append(op.wires[0]) + elif isinstance(op, qml.Identity): + observables += "I" + qubits.append(op.wires[0]) + else: + raise ValueError(f"Unsupported operator type: {type(op)}") + pauli_op = (observables, qubits) + else: + if isinstance(pauli_op, qml.X): + pauli_op = ("X", [pauli_op.wires[0]]) + elif isinstance(pauli_op, qml.Y): + pauli_op = ("Y", [pauli_op.wires[0]]) + elif isinstance(pauli_op, qml.Z): + pauli_op = ("Z", [pauli_op.wires[0]]) + elif isinstance(pauli_op, qml.Identity): + pauli_op = ("I", [pauli_op.wires[0]]) + else: + raise ValueError(f"Unsupported operator type: {type(pauli_op)}") + + terms.append(pauli_op) + try: + logger.debug(f"Terms passed to SparsePauliOp...") + logger.debug(f"Terms: {pp.pformat(terms)}") + logger.debug(f"Coefficients: {pp.pformat(coeffs)}") + logger.debug(f"Number of qubits: {hamiltonian.num_wires+1}") + sparse_pauli_op = SparsePauliOp.from_sparse_list( + [ + (observables, qubits, coeff) + for (observables, qubits), coeff in zip(terms, coeffs) + ], + num_qubits=hamiltonian.num_wires+1, + ) + except QiskitError as e: + raise ValueError(f"Error creating SparsePauliOp: {e}") + + return sparse_pauli_op + + + +if __name__ == "__main__": + hamiltonian = qml.Hamiltonian( + [0.5, 0.5, 0.5], + [ + qml.X(0) @ qml.X(1), + qml.Y(0) @ qml.Y(1), + qml.Z(0) @ qml.Z(1), + ] + ) + + pp.pp(hamiltonian.ops) + + qiskit_hamiltonian = map_pl_hamiltonian_to_qiskit(hamiltonian) + + pp.pp(qiskit_hamiltonian) + + diff --git a/solutions/vqe_qchem/src/plotting.py b/solutions/vqe_qchem/src/plotting.py new file mode 100644 index 0000000..12d7494 --- /dev/null +++ b/solutions/vqe_qchem/src/plotting.py @@ -0,0 +1,92 @@ +import json +import datetime as dt +import logging +from pathlib import Path +import matplotlib.pyplot as plt +from scipy.optimize import OptimizeResult +import numpy as np + +logger = logging.getLogger(__name__) + +RESULTS_DICT = { + "metadata": { + "seed": None, + "optimizer": None, + "max_iterations": None, + "qaoa_layers": None, + }, + "classical": { + "known_parameters": None, + "value": None, + }, + "quantum": { + "energy": None, + "optimal_parameters": None, + "hamiltonian": None, + "optimization_log": {}, + }, + } + +def _plot_optimization_curve( + objective_log: dict[int, dict[str, float]] | list[dict[str, float]], timestamp: str + ) -> None: + """Plot the optimization history of the objective function values and saves the figure.""" + if isinstance(objective_log, dict): + objective_log = [objective_log[key] for key in sorted(objective_log)] + + plt.figure(figsize=(10, 6)) + plt.plot( + [log["estimated_cost"] for log in objective_log], + ) + plt.title(f"Optimization iterations") + plt.xlabel("Iteration") + plt.ylabel("Estimated Cost") + plt.grid() + plot_filename = Path("results") / f"optimization_history_{timestamp}.png" + plt.savefig(plot_filename, dpi=150) + logger.info(f"Optimization history plot saved to {plot_filename}") + plt.close() + +def _json_default(value): + """Convert common non-JSON types in experiment results to plain Python objects.""" + if isinstance(value, complex): + return {"real": value.real, "imag": value.imag} + if isinstance(value, np.ndarray): + return value.tolist() + if isinstance(value, np.generic): + return value.item() + + raise TypeError( + f"Object of type {value.__class__.__name__} is not JSON serializable" + ) + + +def _save_results(results: dict): + """Save the results dictionary to a JSON file with a timestamp. + + Args: + results: The results dictionary to save. + """ + timestamp = dt.datetime.now().strftime("%Y%m%d_%H%M%S") + output_dir = Path("results") + output_dir.mkdir(exist_ok=True) + json_filename = f"vqe_results_{timestamp}.json" + output_file = output_dir / json_filename + + with open(output_file, "w") as f: + json.dump(results, f, indent=4, default=_json_default) + + _plot_optimization_curve(results["quantum"]["optimization_log"], timestamp) + +def process_result(optimization_result: OptimizeResult, results: dict): + """Process the optimization result and update the results dictionary. + + Args: + optimization_result: The optimization result from scipy.optimize.minimize. + results: The dictionary to update with quantum optimization results. + """ + results["quantum"]["final_cost"] = optimization_result.fun + results["quantum"]["optimal_parameters"] = optimization_result.x.tolist() + + + _save_results(results) \ No newline at end of file diff --git a/solutions/vqe_qchem/src/vqa_hydroxide.py b/solutions/vqe_qchem/src/vqa_hydroxide.py new file mode 100644 index 0000000..3f3aba3 --- /dev/null +++ b/solutions/vqe_qchem/src/vqa_hydroxide.py @@ -0,0 +1,187 @@ +import numpy as np +from qiskit import QuantumCircuit +from qiskit.circuit import Parameter +from qiskit.circuit.library import n_local +from qiskit.quantum_info import SparsePauliOp +from qiskit.primitives import StatevectorEstimator +from scipy.optimize import minimize, OptimizeResult +from ham_sdk_conversion import map_pl_hamiltonian_to_qiskit +import plotting as plot +import pennylane as qml +import pprint as pp +import logging +from argparse import ArgumentParser + +_PACKAGE_LOGGER_NAME = __package__ or __name__ +_LOG_FORMAT = "%(asctime)s - %(levelname)s - %(name)s - %(message)s" + + +def _configure_package_logger() -> logging.Logger: + package_logger = logging.getLogger(_PACKAGE_LOGGER_NAME) + package_logger.setLevel(logging.INFO) + + if not package_logger.handlers: + handler = logging.StreamHandler() + handler.setFormatter(logging.Formatter(_LOG_FORMAT)) + package_logger.addHandler(handler) + + # Keep this logging setup local to this package logger. + package_logger.propagate = False + return package_logger + + +logger = _configure_package_logger() + + +def classical_diagonalization(hamiltonian: SparsePauliOp) -> float: + """Classically diagonalize the Hamiltonian to find the ground state energy. + + Args: + hamiltonian: Hamiltonian operator to diagonalize. + + Returns: + float: Ground state energy of the Hamiltonian. + """ + # Convert the SparsePauliOp to a dense matrix + dense_matrix = hamiltonian.to_matrix() + + # Diagonalize the matrix to find eigenvalues + eigenvalues, _ = np.linalg.eigh(dense_matrix) + + # The ground state energy is the minimum eigenvalue + ground_state_energy = np.min(eigenvalues) + + return ground_state_energy + +def vqe_loop(input_hamiltonian: SparsePauliOp, optimizer: str, iterations: int, ansatz_layers: int, seed: int) -> OptimizeResult: + """Run a VQE optimization using only the most basic Qiskit components. + + Args: + input_hamiltonian: Hamiltonian operator to minimize. + optimizer: Optimizer to use for parameter optimization. + iterations: Number of iterations for the optimizer. + ansatz_layers: Number of ansatz layers (reps). + seed: Random seed for reproducibility. + + Returns: + scipy.optimize.OptimizeResult: Optimization result returned by ``minimize``. + + Example: + >>> from hamiltonian import Hamiltonian + >>> result = vqe_bare(Hamiltonian.H2_STO6G_REDUX.value) + """ + + # Define the ansatz + ansatz = n_local( + num_qubits = input_hamiltonian.num_qubits, # type: ignore + rotation_blocks = ["rx", "rz"], + entanglement_blocks = ["cx"], + reps = ansatz_layers, + entanglement = "linear", + ) + + # Build the Hamiltonian + hamiltonian = input_hamiltonian + + # Set up the estimator + estimator = StatevectorEstimator(seed=seed) + + # Define the cost function for optimization + def cost_function( + params, ansatz=ansatz, hamiltonian=hamiltonian, estimator=estimator + ) -> float: + pub_estimate = ( + ansatz, + [hamiltonian], + [params], + ) + result = estimator.run( + pubs=[pub_estimate], + ).result() + energy = result[0].data.evs[0] + num_evaluations = len(plot.RESULTS_DICT["quantum"]["optimization_log"]) + 1 + plot.RESULTS_DICT["quantum"]["optimization_log"][num_evaluations] = { + "parameters": params.tolist(), + "estimated_cost": energy, + } + return energy + + # Run the optimization + initial_params = np.random.RandomState(seed=seed).rand(ansatz.num_parameters) # type: ignore + + optimization_result = minimize( + cost_function, + x0=initial_params, + args=(ansatz, hamiltonian, estimator), + method=optimizer, + options={"maxiter": iterations}, + callback=lambda xk: logger.info(f"Current parameters: {xk}, Current energy: {cost_function(xk)}"), + ) + + return optimization_result + +def hydroxide_solver(args): + """Run the VQE optimization for the hydroxide ion (OH-). + + Args: + args: Command-line arguments containing optimizer settings. + + Returns: + scipy.optimize.OptimizeResult: Optimization result returned by ``minimize``. + """ + hydroxide_dataset = qml.data.load("qchem", molname="OH-", bondlength=0.964, basis="STO-3G") + + logger.debug(f"Tapered Hamiltonian: {hydroxide_dataset[0].tapered_hamiltonian}") + hamiltonian = map_pl_hamiltonian_to_qiskit(hydroxide_dataset[0].tapered_hamiltonian) + plot.RESULTS_DICT["quantum"]["hamiltonian"] = hamiltonian.to_list() + + classical_energy = classical_diagonalization(hamiltonian) + plot.RESULTS_DICT["classical"]["value"] = classical_energy + + logger.info(f"Classical diagonalization energy: {classical_energy:.6f} Hartree") + + # Run the VQE optimization + results = vqe_loop(hamiltonian, optimizer=args.optimizer, iterations=args.iter, ansatz_layers=args.ansatz_layers, seed=args.seed) + + logger.info(f"VQE optimization result: {results}") + + plot.process_result(results, plot.RESULTS_DICT) + + + +if __name__ == "__main__": + # Parser for command-line arguments + parser = ArgumentParser(description="Run VQE optimization for the hydroxide ion (OH-).") + parser.add_argument( + "--optimizer", + choices=["COBYLA", "SLSQP", "ADAM"], + default="COBYLA", + help="Optimizer to use for parameter optimization", + ) + parser.add_argument( + "--iter", + type=int, + default=100, + help="Number of iterations for the optimizer", + ) + parser.add_argument( + "--ansatz-layers", + type=int, + default=1, + help="Number of ansatz layers (reps)", + ) + parser.add_argument( + "--seed", + type=int, + default=67, + help="Random seed for reproducibility", + ) + args = parser.parse_args() + + # save parameters in results dict + plot.RESULTS_DICT["metadata"]["seed"] = args.seed + plot.RESULTS_DICT["metadata"]["optimizer"] = args.optimizer + plot.RESULTS_DICT["metadata"]["max_iterations"] = args.iter + plot.RESULTS_DICT["metadata"]["qaoa_layers"] = args.ansatz_layers + + hydroxide_solver(args=args) \ No newline at end of file diff --git a/uv.lock b/uv.lock index c3a6951..d66e9a6 100644 --- a/uv.lock +++ b/uv.lock @@ -4,6 +4,7 @@ requires-python = ">=3.12" [manifest] members = [ + "ibm-runtime", "iqucodefest-practice", "qnn-mnist", "vqa-knapsack", @@ -149,6 +150,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/fb/76/641ae371508676492379f16e2fa48f4e2c11741bd63c48be4b12a6b09cba/aiosignal-1.4.0-py3-none-any.whl", hash = "sha256:053243f8b92b990551949e63930a839ff0cf0b0ebbe0597b0f3fb19e1a0fe82e", size = 7490, upload-time = "2025-07-03T22:54:42.156Z" }, ] +[[package]] +name = "annotated-types" +version = "0.7.0" +source = { registry = 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