{ "cells": [ { "cell_type": "markdown", "id": "b6d1e3ec", "metadata": {}, "source": [ "---\n", "title: \"Courbes de dissociation PES avec Qunova HiVQE\"\n", "description: \"Utilisez l' HiVQE e Qunova pour calculer les courbes de surface d'énergie potentielle de dissociation (PES).\"\n", "---\n", "\n", "{/* cspell:ignore fontsize labelsize DMRG hivqe mcscf CASCI */}\n", "\n", "\n", "\n", "# Courbes de dissociation PES avec Qunova HiVQE\n", "\n" ] }, { "attachments": {}, "cell_type": "markdown", "id": "a6f69b77", "metadata": {}, "source": [ "\n", " Les fonctions Qiskit sont une fonctionnalité expérimentale disponible uniquement pour IBM Quantum® Premium Plan, Flex Plan, et On-Prem (via IBM Quantum Platform API). Elles sont en cours de publication et peuvent être modifiées.\n", "\n", "\n", "*Estimation de l'utilisation (NOTE : Il s'agit uniquement d'une estimation. Votre durée d'exécution peut varier.)*\n", "\n", "* Li2S: Cinq minutes de QPU sur un processeur Heron r2\n", "* FeP-NO: Cinq minutes de QPU sur un processeur Heron r2\n", "\n" ] }, { "cell_type": "markdown", "id": "8bf80006", "metadata": {}, "source": [ "\n", "\n", "## Arrière-plan\n", "\n", "Le calcul précis des énergies de réaction chimique est crucial pour les avancées scientifiques dans les domaines de la science des matériaux, du génie chimique, de la découverte de médicaments et d'autres domaines. Parmi les différents systèmes chimiques, le système Li-S a suscité un grand intérêt pour la compréhension et le développement de nouvelles compositions de batteries. Ce tutoriel fournit une expérience pratique du calcul de la surface d'énergie potentielle de dissociation de la liaison Li-S (PES) d'un système $Li_2S$ en retirant un atome de lithium à l'aide des calculs HiVQE. Les résultats peuvent être comparés à des calculs de référence (CASCI) ainsi qu'à des méthodes classiques telles que Hartree-Fock (HF) pour un problème de 20 qubits.\n", "\n" ] }, { "cell_type": "markdown", "id": "55b94021", "metadata": {}, "source": [ "\n", "\n", "## Exigences\n", "\n", "Installez les dépendances suivantes pour exécuter le code de ce tutoriel.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "0a778d85", "metadata": {}, "outputs": [], "source": [ "!pip install --upgrade pip\n", "!pip install -U qiskit-ibm-catalog \"qiskit_ibm_runtime<0.42.0\" pyscf numpy matplotlib typing_extensions" ] }, { "attachments": {}, "cell_type": "markdown", "id": "7db2e559", "metadata": {}, "source": [ "\n", "\n", "## Configuration\n", "\n", "Pour exécuter ce tutoriel, importez la fonction `qunova/hivqe-chemistry` via `QiskitFunctionCatalog`. Vous devez disposer d'un compte IBM Quantum Premium Plan, Flex Plan ou On-Prem ( IBM Quantum Platform API) Plan avec une licence de Qunova pour exécuter cette fonction.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "bc380c46", "metadata": {}, "outputs": [], "source": [ "from qiskit_ibm_catalog import QiskitFunctionsCatalog\n", "from pyscf import gto, scf, mcscf\n", "import matplotlib.pyplot as plt\n", "import pprint\n", "\n", "catalog = QiskitFunctionsCatalog(\n", " channel=\"ibm_quantum_platform\",\n", " instance=\"INSTANCE_CRN\",\n", " # For `token`, use the 44-character API_KEY you created\n", " # and saved from the IBM Quantum Platform Home dashboard\n", " token=\"YOUR_API_KEY\",\n", ")\n", "\n", "hivqe = catalog.load(\"qunova/hivqe-chemistry\")" ] }, { "attachments": {}, "cell_type": "markdown", "id": "988ee237", "metadata": {}, "source": [ "\n", "\n", "## Partie 1 : Li2S ( 20Q )\n", "\n", "\n", "\n", "### Étape 1 : Mettre en correspondance les entrées classiques avec un problème quantique\n", "\n", "Définir les géométries $Li_2S$ sous forme de dictionnaire pour différentes distances de liaison Li-S afin de calculer la courbe PES. Ces géométries sont optimisées à l'aide des calculs B3LYP/631g.\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "14dcfa48", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'1.51': 'S -1.239044 0.671232 -0.030374; Li -1.506327 0.432403 -1.498949; Li -0.899996 0.973348 1.826768',\n", " '1.91': 'S -1.215858 0.692272 0.099232; Li -1.553305 0.390283 -1.758043; Li -0.876205 0.994426 1.956257',\n", " '2.40': 'S -1.741432 0.680397 0.346702; Li -0.529307 0.488006 -1.729343; Li -1.284307 0.989409 2.177209',\n", " '3.10': 'S -2.347450 0.657089 0.566194; Li -0.199353 0.527517 -1.665148; Li -1.008243 0.973206 1.893522',\n", " '3.80': 'S -2.707255 0.674298 0.909161; Li 0.079218 0.552012 -1.671656; Li -0.927010 0.931502 1.557063',\n", " '4.50': 'S -2.913363 0.709175 1.276987; Li 0.368656 0.559989 -1.798088; Li -1.010340 0.888647 1.315670'}" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "str_geometries = {\n", " \"1.51\": \"S -1.239044 0.671232 -0.030374; Li -1.506327 0.432403 -1.498949; Li -0.899996 0.973348 1.826768\",\n", " \"1.91\": \"S -1.215858 0.692272 0.099232; Li -1.553305 0.390283 -1.758043; Li -0.876205 0.994426 1.956257\",\n", " \"2.40\": \"S -1.741432 0.680397 0.346702; Li -0.529307 0.488006 -1.729343; Li -1.284307 0.989409 2.177209\",\n", " \"3.10\": \"S -2.347450 0.657089 0.566194; Li -0.199353 0.527517 -1.665148; Li -1.008243 0.973206 1.893522\",\n", " \"3.80\": \"S -2.707255 0.674298 0.909161; Li 0.079218 0.552012 -1.671656; Li -0.927010 0.931502 1.557063\",\n", " \"4.50\": \"S -2.913363 0.709175 1.276987; Li 0.368656 0.559989 -1.798088; Li -1.010340 0.888647 1.315670\",\n", "}\n", "str_geometries" ] }, { "cell_type": "markdown", "id": "3cd2cc70", "metadata": {}, "source": [ "HiVQE les calculs seront effectués avec les options définies ci-dessous. En utilisant la base sto3g pour $Li_2S$, il y a 19 orbitales spatiales avec 22 électrons. Pour exécuter le cas ( 10o,10e ) avec le calcul HiVQE, vous pouvez définir 10 orbitales actives et six orbitales gelées. À chaque itération, 100 tirs seront utilisés pour échantillonner la configuration électronique générée par le circuit quantique ExcitationPreserving (`epa`) avec `circular` entanglement et deux répétitions (`reps`). Le nombre maximum d'itérations est fixé à 30 pour garantir la fin de l'itération avec une convergence énergétique.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "52833c26", "metadata": {}, "outputs": [], "source": [ "molecule_options = {\n", " \"basis\": \"sto3g\",\n", " \"active_orbitals\": list(range(5, 15)),\n", " \"frozen_orbitals\": list(range(5)),\n", "}\n", "\n", "hivqe_options = {\n", " \"shots\": 100,\n", " \"max_iter\": 30,\n", " \"ansatz\": \"epa\",\n", " \"ansatz_entanglement\": \"circular\",\n", " \"ansatz_reps\": 2,\n", "}" ] }, { "cell_type": "markdown", "id": "3ee5ec4c", "metadata": {}, "source": [ "\n", "\n", "### Étapes 2 et 3 : Optimiser le problème pour l'exécution sur du matériel quantique et l'exécuter à l'aide de la fonction Chimie d' HiVQE\n", "\n", "Configurer la boucle `for` pour effectuer des calculs HiVQE avec des géométries dont les options sont définies ci-dessous. Les emplois sont soumis dans la boucle `for` . Dans ce tutoriel, vous soumettrez six géométries et récupérerez les résultats lorsqu'elles seront toutes terminées. Dans l'exécution de la fonction principale, vous devez définir `max_states` et `max_expansion_states` pour contrôler la taille maximale de la matrice du sous-espace et pour contrôler le nombre d'états pouvant être générés à l'aide des méthodes classiques d'expansion de l'IC par itération. Les identifiants des fonctions seront stockés dans le dictionnaire avec chaque étiquette géométrique afin de poursuivre le suivi et le traitement de l'ouptut.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "39109638", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'1.51': 'de3b8818-c9db-4fa3-a3c2-d51551c2dfaf', '1.91': '55d9467a-fc85-49a8-9bc6-8f6990e421e5', '2.40': '415112b3-69ff-4d53-8b10-cb4e3be68c9e', '3.10': 'ef67b600-3887-4225-b872-e354dfdf8454', '3.80': 'b16d3502-a9e4-4560-9775-852e9d07e70f', '4.50': '0c0bffc7-af77-4a56-a656-2a2610c991d6'}\n" ] } ], "source": [ "info_jobid = {}\n", "for dis, geom in str_geometries.items():\n", " hivqe_run = hivqe.run(\n", " geometry=geom,\n", " backend_name=\"\",\n", " max_states=40000,\n", " max_expansion_states=100,\n", " molecule_options=molecule_options,\n", " hivqe_options=hivqe_options,\n", " )\n", " status = hivqe_run.status()\n", " info_jobid[dis] = hivqe_run.job_id\n", "print(info_jobid)" ] }, { "cell_type": "markdown", "id": "5f48bee4", "metadata": {}, "source": [ "Vérifions si tous les travaux sont toujours en cours ou terminés.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "f3be3b7b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.51 de3b8818-c9db-4fa3-a3c2-d51551c2dfaf DONE\n", "1.91 55d9467a-fc85-49a8-9bc6-8f6990e421e5 DONE\n", "2.40 415112b3-69ff-4d53-8b10-cb4e3be68c9e DONE\n", "3.10 ef67b600-3887-4225-b872-e354dfdf8454 DONE\n", "3.80 b16d3502-a9e4-4560-9775-852e9d07e70f DONE\n", "4.50 0c0bffc7-af77-4a56-a656-2a2610c991d6 DONE\n", "Completed 6 job, Running or Queued 0 job\n" ] } ], "source": [ "completed_jobs_num = 0\n", "running_jobs_num = 0\n", "completed_jobs = {}\n", "for i, info in enumerate(info_jobid.items()):\n", " dis, job_id = info\n", " submitted_job = catalog.get_job_by_id(job_id)\n", " stat = submitted_job.status()\n", " print(dis, submitted_job.job_id, stat)\n", " if stat == \"DONE\":\n", " completed_jobs_num += 1\n", " completed_jobs[dis] = submitted_job\n", " if (stat == \"RUNNING\") or (stat == \"QUEUED\"):\n", " running_jobs_num += 1\n", "\n", "print(\n", " f\"Completed {completed_jobs_num} job, \"\n", " f\"Running or Queued {running_jobs_num} job\"\n", ")" ] }, { "cell_type": "markdown", "id": "fb4264d5", "metadata": {}, "source": [ "Une fois que tous les travaux sont terminés, récupérons tous les résultats des calculs.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "7e7adaba", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "All jobs are completed\n", "1.51 -407.8944801731773\n", "1.91 -407.9800570932916\n", "2.40 -407.9372992999806\n", "3.10 -407.86278336000134\n", "3.80 -407.83092972296157\n", "4.50 -407.82971011225766\n" ] } ], "source": [ "hivqe_result = {}\n", "if len(info_jobid) == completed_jobs_num:\n", " print(\"All jobs are completed\")\n", " for i, job in enumerate(completed_jobs.items()):\n", " dis, cal = job\n", " print(dis, cal.result()[\"energy\"])\n", " hivqe_result[str(dis)] = cal.result()[\"energy\"]" ] }, { "cell_type": "code", "execution_count": null, "id": "dc9982c5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'1.51': -407.8944801731773,\n", " '1.91': -407.9800570932916,\n", " '2.40': -407.9372992999806,\n", " '3.10': -407.86278336000134,\n", " '3.80': -407.83092972296157,\n", " '4.50': -407.82971011225766}\n" ] } ], "source": [ "pprint.pprint(hivqe_result)" ] }, { "cell_type": "markdown", "id": "cc7c2793", "metadata": {}, "source": [ "La durée d'exécution totale de la QPU utilisée dans le travail peut être suivie en se connectant à [IBM Quantum Platform](/) et en visualisant les travaux soumis avec l'étiquette `qunova-chemistry-hivqe` .\n", "\n" ] }, { "cell_type": "markdown", "id": "75fc5cf6", "metadata": {}, "source": [ "\n", "\n", "### Étape 4 : Post-traitement et comparaison avec les méthodes classiques\n", "\n", "Le calcul de référence classique (CASCI) peut être effectué pour ( 10o,10e ) afin de valider les résultats de HiVQE.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "3310e3b8", "metadata": {}, "outputs": [], "source": [ "str_geometries = {\n", " \"1.31\": \"S -1.250686 0.660708 -0.095168; Li -1.482812 0.453464 -1.369406; Li -0.911870 0.962810 1.762020\",\n", " \"1.41\": \"S -1.244856 0.665971 -0.062773; Li -1.494574 0.442933 -1.434177; Li -0.905937 0.968078 1.794395\",\n", " \"1.51\": \"S -1.239044 0.671232 -0.030374; Li -1.506327 0.432403 -1.498949; Li -0.899996 0.973348 1.826768\",\n", " \"1.61\": \"S -1.233245 0.676492 0.002027; Li -1.518073 0.421873 -1.563722; Li -0.894049 0.978617 1.859141\",\n", " \"1.71\": \"S -1.227453 0.681752 0.034429; Li -1.529816 0.411343 -1.628496; Li -0.888099 0.983887 1.891513\",\n", " \"1.81\": \"S -1.221659 0.687012 0.066831; Li -1.541558 0.400813 -1.693270; Li -0.882150 0.989157 1.923885\",\n", " \"1.91\": \"S -1.215858 0.692272 0.099232; Li -1.553305 0.390283 -1.758043; Li -0.876205 0.994426 1.956257\",\n", " \"2.01\": \"S -1.209887 0.697544 0.131599; Li -1.565136 0.379748 -1.822800; Li -0.870344 0.999691 1.988646\",\n", " \"2.11\": \"S -1.203945 0.702813 0.163973; Li -1.576953 0.369214 -1.887560; Li -0.864469 1.004956 2.021033\",\n", " \"2.21\": \"S -1.198023 0.708081 0.196350; Li -1.588760 0.358680 -1.952322; Li -0.858584 1.010221 2.053417\",\n", " \"2.30\": \"S -1.365426 0.717714 0.367060; Li -0.689401 0.458925 -1.828368; Li -1.500219 0.981173 2.255876\",\n", " \"2.31\": \"S -1.192118 0.713348 0.228731; Li -1.600559 0.348146 -2.017085; Li -0.852690 1.015488 2.085800\",\n", " \"2.40\": \"S -1.741432 0.680397 0.346702; Li -0.529307 0.488006 -1.729343; Li -1.284307 0.989409 2.177209\",\n", " \"2.50\": \"S -1.885961 0.669986 0.365815; Li -0.461563 0.499084 -1.695846; Li -1.207523 0.988741 2.124599\",\n", " \"2.60\": \"S -1.977163 0.665155 0.389784; Li -0.416654 0.504966 -1.683655; Li -1.161229 0.987690 2.088439\",\n", " \"2.70\": \"S -2.063642 0.661518 0.418977; Li -0.367600 0.510505 -1.676408; Li -1.123804 0.985788 2.051998\",\n", " \"2.80\": \"S -2.141072 0.659218 0.451663; Li -0.323153 0.515056 -1.673046; Li -1.090821 0.983538 2.015951\",\n", " \"2.90\": \"S -2.212097 0.657968 0.487535; Li -0.281989 0.518909 -1.672407; Li -1.060960 0.980935 1.979440\",\n", " \"3.00\": \"S -2.281477 0.657123 0.525155; Li -0.239607 0.523326 -1.668669; Li -1.033963 0.977363 1.938081\",\n", " \"3.10\": \"S -2.347450 0.657089 0.566194; Li -0.199353 0.527517 -1.665148; Li -1.008243 0.973206 1.893522\",\n", " \"3.20\": \"S -2.410882 0.657532 0.608912; Li -0.157788 0.532069 -1.659971; Li -0.986376 0.968211 1.845627\",\n", " \"3.30\": \"S -2.470306 0.658818 0.654893; Li -0.118007 0.536237 -1.656311; Li -0.966733 0.962757 1.795986\",\n", " \"3.40\": \"S -2.525776 0.660762 0.702910; Li -0.078312 0.540189 -1.654076; Li -0.950958 0.956861 1.745734\",\n", " \"3.50\": \"S -2.576885 0.663376 0.752788; Li -0.039076 0.543706 -1.654536; Li -0.939085 0.950730 1.696316\",\n", " \"3.60\": \"S -2.623930 0.666534 0.803853; Li 0.000274 0.546839 -1.657697; Li -0.931390 0.944439 1.648412\",\n", " \"3.70\": \"S -2.667364 0.670217 0.856250; Li 0.039572 0.549616 -1.663265; Li -0.927254 0.937980 1.601583\",\n", " \"3.80\": \"S -2.707255 0.674298 0.909161; Li 0.079218 0.552012 -1.671656; Li -0.927010 0.931502 1.557063\",\n", " \"3.90\": \"S -2.744005 0.678718 0.962425; Li 0.119268 0.554073 -1.682595; Li -0.930310 0.925021 1.514738\",\n", " \"4.00\": \"S -2.777891 0.683415 1.015798; Li 0.159751 0.555810 -1.696024; Li -0.936907 0.918587 1.474794\",\n", " \"4.10\": \"S -2.809179 0.688333 1.069057; Li 0.200678 0.557234 -1.711873; Li -0.946546 0.912245 1.437385\",\n", " \"4.20\": \"S -2.838194 0.693443 1.122205; Li 0.242066 0.558401 -1.729770; Li -0.958918 0.905968 1.402134\",\n", " \"4.30\": \"S -2.864984 0.698619 1.174415; Li 0.283858 0.559186 -1.750539; Li -0.973920 0.900007 1.370693\",\n", " \"4.40\": \"S -2.889984 0.703887 1.226140; Li 0.326068 0.559728 -1.773231; Li -0.991131 0.894196 1.341660\",\n", " \"4.50\": \"S -2.913363 0.709175 1.276987; Li 0.368656 0.559989 -1.798088; Li -1.010340 0.888647 1.315670\",\n", "}" ] }, { "cell_type": "code", "execution_count": null, "id": "95e7040f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "d=1.3 RHF Energy: -407.7137006, CASCI Energy: -407.7193917\n", "d=1.4 RHF Energy: -407.8183196, CASCI Energy: -407.8245211\n", "d=1.5 RHF Energy: -407.8878013, CASCI Energy: -407.8944802\n", "d=1.6 RHF Energy: -407.9315356, CASCI Energy: -407.9385663\n", "d=1.7 RHF Energy: -407.9569034, CASCI Energy: -407.9641258\n", "d=1.8 RHF Energy: -407.9693681, CASCI Energy: -407.9766313\n", "d=1.9 RHF Energy: -407.9728592, CASCI Energy: -407.9800572\n", "d=2.0 RHF Energy: -407.9701684, CASCI Energy: -407.9772549\n", "d=2.1 RHF Energy: -407.9632701, CASCI Energy: -407.9702381\n", "d=2.2 RHF Energy: -407.9535584, CASCI Energy: -407.9604007\n", "d=2.3 RHF Energy: -407.9420173, CASCI Energy: -407.9487043\n", "d=2.3 RHF Energy: -407.9420156, CASCI Energy: -407.9487024\n", "d=2.4 RHF Energy: -407.9297216, CASCI Energy: -407.9372993\n", "d=2.5 RHF Energy: -407.9172, CASCI Energy: -407.9261859\n", "d=2.6 RHF Energy: -407.9061139, CASCI Energy: -407.915961\n", "d=2.7 RHF Energy: -407.8937118, CASCI Energy: -407.904259\n", "d=2.8 RHF Energy: -407.8816389, CASCI Energy: -407.8928292\n", "d=2.9 RHF Energy: -407.8700448, CASCI Energy: -407.8819574\n", "d=3.0 RHF Energy: -407.859054, CASCI Energy: -407.8719092\n", "d=3.1 RHF Energy: -407.8487619, CASCI Energy: -407.8628304\n", "d=3.2 RHF Energy: -407.8392304, CASCI Energy: -407.8548482\n", "d=3.3 RHF Energy: -407.8304842, CASCI Energy: -407.8480217\n", "d=3.4 RHF Energy: -407.8225124, CASCI Energy: -407.8423743\n", "d=3.5 RHF Energy: -407.8152758, CASCI Energy: -407.8378892\n", "d=3.6 RHF Energy: -407.8087161, CASCI Energy: -407.8345331\n", "d=3.7 RHF Energy: -407.802764, CASCI Energy: -407.8322563\n", "d=3.8 RHF Energy: -407.7973458, CASCI Energy: -407.83093\n", "d=3.9 RHF Energy: -407.7923883, CASCI Energy: -407.8303555\n", "d=4.0 RHF Energy: -407.7878216, CASCI Energy: -407.83025\n", "d=4.1 RHF Energy: -407.783582, CASCI Energy: -407.8303243\n", "d=4.2 RHF Energy: -407.7796124, CASCI Energy: -407.8303791\n", "d=4.3 RHF Energy: -407.7758633, CASCI Energy: -407.8302885\n", "d=4.4 RHF Energy: -407.7722923, CASCI Energy: -407.8300614\n", "d=4.5 RHF Energy: -407.7688641, CASCI Energy: -407.829711\n" ] } ], "source": [ "rhf_result = {}\n", "casci_result = {}\n", "\n", "cas_list = molecule_options[\"active_orbitals\"]\n", "distance_ref = []\n", "for dis, geom in str_geometries.items():\n", " distance_ref.append(dis)\n", " mole = gto.M(atom=geom, basis=molecule_options[\"basis\"])\n", " mole.verbose = 0\n", " # RHF energy\n", " mf = scf.RHF(mole).run()\n", " mo_occ = mf.mo_occ\n", " num_elecs_as = int(sum([mo_occ[idx] for idx in cas_list]))\n", "\n", " rhf_result[str(dis)] = mf.e_tot\n", " # CASCI energy\n", " casci_solver = mcscf.CASCI(mf, len(cas_list), num_elecs_as)\n", " orbs = mcscf.addons.sort_mo(casci_solver, mf.mo_coeff, cas_list, base=0)\n", " casci_solver.kernel(orbs)\n", " casci_result[str(dis)] = casci_solver.e_tot\n", " print(\n", " f\"d={dis:4.3} RHF Energy: {mf.e_tot:14.10}, \"\n", " f\"CASCI Energy: {casci_solver.e_tot:14.10}\"\n", " )" ] }, { "cell_type": "markdown", "id": "e1900a94", "metadata": {}, "source": [ "\n", "\n", "### Tracé de la courbe de dissociation de l' Li\\_2S\n", "\n", "Traçons et comparons les résultats de HiVQE avec HF et CASCI.\n", "Vous pouvez observer que tous les calculs de HiVQE correspondent bien au résultat de la référence classique (CASCI).\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "2db64a7d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\"Output" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 1)\n", "hf_energy = [v for key, v in rhf_result.items()]\n", "casci_energy = [v for key, v in casci_result.items()]\n", "hivqe_energy = [v for key, v in hivqe_result.items()]\n", "distance_ref = [float(key) for key, v in rhf_result.items()]\n", "distance = [float(key) for key, v in hivqe_result.items()]\n", "\n", "ax.plot(distance_ref, hf_energy, \"-o\", label=\"RHF\", c=\"blue\")\n", "ax.plot(distance_ref, casci_energy, \"-o\", label=\"CASCI\", c=\"green\")\n", "ax.plot(distance, hivqe_energy, \"x\", label=\"HiVQE\", c=\"red\", markersize=20)\n", "ax.legend(fontsize=20)\n", "ax.tick_params(\"both\", labelsize=16)\n", "ax.set_xlabel(\"Bond distance (angstrom)\", size=20)\n", "ax.set_ylabel(\"Energy (Ha)\", size=20)\n", "ax.set_title(\"Li2S PES curve\", size=20)\n", "fig.set_size_inches(14, 8)" ] }, { "cell_type": "markdown", "id": "46379f8a", "metadata": {}, "source": [ "\n", "\n", "## Partie 2 : FeP-NO ( 44Q )\n", "\n", "Notez que pour exécuter la fonction sur le système FeP-NO avec les paramètres indiqués dans cet exemple, vous devez disposer d'une licence autorisant l'utilisation de la fonction avec au moins 44 qubits. Envoyez un e-mail [à](mailto:qiskit.support@qunovacomputing.com) qiskit.support @ qunovacomputing.com pour obtenir des informations sur l'obtention d'une licence.\n", "\n", "\n", "\n", "### Étape 1 : Mettre en correspondance les entrées classiques avec un problème quantique\n", "\n", "Définir les options pour les calculs HiVQE\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "39bce095", "metadata": {}, "outputs": [], "source": [ "molecule_options = {\n", " \"basis\": \"631g*\",\n", " \"active_orbitals\": list(range(90, 112, 1)),\n", " \"frozen_orbitals\": list(range(0, 90, 1)),\n", " \"charge\": -1,\n", "}\n", "\n", "hivqe_options = {\n", " \"shots\": 2000,\n", " \"max_iter\": 40,\n", " \"ansatz\": \"epa\",\n", " \"ansatz_entanglement\": \"linear\",\n", " \"ansatz_reps\": 2,\n", " \"amplitude_screening_tolerance\": 1e-6,\n", "}" ] }, { "cell_type": "markdown", "id": "e7c29d79", "metadata": {}, "source": [ "Définir les géométries FeP-NO sous forme de dictionnaire pour différentes distances de liaison Fe-N afin de calculer la courbe PES.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "8c93344d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'5.0': '\\nFe 9.918629 31.289202 1.717339\\nN 10.542914 31.832173 -0.080685\\nN 11.795572 31.199413 2.341831\\nN 9.294593 30.741247 3.513929\\nN 8.042689 31.359481 1.087282\\nC 9.775254 32.111817 -1.200449\\nC 10.600219 32.479101 -2.319680\\nC 11.891090 32.425876 -1.887580\\nC 11.847694 32.024341 -0.507342\\nC 12.945734 31.464689 1.611366\\nC 14.116395 31.289997 2.423572\\nC 13.685777 30.915122 3.663719\\nC 12.252381 30.861042 3.608186\\nC 10.062170 30.463021 4.634102\\nC 9.236749 30.104333 5.755782\\nC 7.945687 30.161198 5.324720\\nC 7.989641 30.552269 3.941498\\nC 6.892881 31.087489 1.815829\\nC 5.722676 31.253502 1.001149\\nC 6.153153 31.631057 -0.238233\\nC 7.586010 31.695401 -0.179773\\nC 8.390724 32.047572 -1.247553\\nH 7.903308 32.291586 -2.187969\\nC 12.973334 31.849872 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geometry_2_00,\n", " \"5.00\": geometry_5_00,\n", "}\n", "\n", "\n", "hivqe_result = {}" ] }, { "cell_type": "code", "execution_count": null, "id": "52b55a07", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'5.0': '\\nFe 9.918629 31.289202 1.717339\\nN 10.542914 31.832173 -0.080685\\nN 11.795572 31.199413 2.341831\\nN 9.294593 30.741247 3.513929\\nN 8.042689 31.359481 1.087282\\nC 9.775254 32.111817 -1.200449\\nC 10.600219 32.479101 -2.319680\\nC 11.891090 32.425876 -1.887580\\nC 11.847694 32.024341 -0.507342\\nC 12.945734 31.464689 1.611366\\nC 14.116395 31.289997 2.423572\\nC 13.685777 30.915122 3.663719\\nC 12.252381 30.861042 3.608186\\nC 10.062170 30.463021 4.634102\\nC 9.236749 30.104333 5.755782\\nC 7.945687 30.161198 5.324720\\nC 7.989641 30.552269 3.941498\\nC 6.892881 31.087489 1.815829\\nC 5.722676 31.253502 1.001149\\nC 6.153153 31.631057 -0.238233\\nC 7.586010 31.695401 -0.179773\\nC 8.390724 32.047572 -1.247553\\nH 7.903308 32.291586 -2.187969\\nC 12.973334 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1.606023\n", "\"\"\"\n", "\n", "geometry_2_00 = \"\"\"\n", "Fe 9.917990 31.445558 1.778346\n", "N 10.556809 31.866188 -0.055498\n", "N 11.814089 31.227003 2.372666\n", "N 9.297875 30.758246 3.550104\n", "N 8.043584 31.397768 1.120485\n", "C 9.784831 32.164652 -1.160219\n", "C 10.611624 32.501801 -2.293514\n", "C 11.902858 32.406547 -1.875160\n", "C 11.859552 32.017818 -0.486307\n", "C 12.960503 31.454432 1.636717\n", "C 14.140770 31.242960 2.439615\n", "C 13.708543 30.884151 3.678983\n", "C 12.266351 30.874173 3.627468\n", "C 10.070264 30.465070 4.655102\n", "C 9.247247 30.053101 5.766681\n", "C 7.958085 30.091201 5.332866\n", "C 7.998432 30.529979 3.958727\n", "C 6.901428 31.093932 1.833807\n", "C 5.723289 31.255057 1.016540\n", "C 6.151314 31.670649 -0.206350\n", "C 7.589736 31.755538 -0.133074\n", "C 8.400230 32.124963 -1.194447\n", "H 7.913264 32.386655 -2.130914\n", "C 12.983905 31.827747 0.302415\n", "H 13.955696 31.979687 -0.161365\n", "C 11.454251 30.533644 4.698234\n", "H 11.941002 30.276716 5.636156\n", "C 6.877444 30.689985 3.159940\n", "H 5.907605 30.480118 3.604825\n", "H 12.813105 32.581608 -2.437367\n", "H 10.233725 32.768337 -3.273979\n", "H 15.157796 31.357524 2.082132\n", "H 14.295001 30.638320 4.557047\n", "H 9.626721 29.768762 6.741623\n", "H 7.051752 29.847502 5.875478\n", "H 4.709710 31.071712 1.354640\n", "H 5.565103 31.898376 -1.089333\n", "N 9.840508 33.353531 2.373019\n", "O 9.344561 34.158205 1.637232\n", "\"\"\"\n", "\n", "geometry_5_00 = \"\"\"\n", "Fe 9.918629 31.289202 1.717339\n", "N 10.542914 31.832173 -0.080685\n", "N 11.795572 31.199413 2.341831\n", "N 9.294593 30.741247 3.513929\n", "N 8.042689 31.359481 1.087282\n", "C 9.775254 32.111817 -1.200449\n", "C 10.600219 32.479101 -2.319680\n", "C 11.891090 32.425876 -1.887580\n", "C 11.847694 32.024341 -0.507342\n", "C 12.945734 31.464689 1.611366\n", "C 14.116395 31.289997 2.423572\n", "C 13.685777 30.915122 3.663719\n", "C 12.252381 30.861042 3.608186\n", "C 10.062170 30.463021 4.634102\n", "C 9.236749 30.104333 5.755782\n", "C 7.945687 30.161198 5.324720\n", "C 7.989641 30.552269 3.941498\n", "C 6.892881 31.087489 1.815829\n", "C 5.722676 31.253502 1.001149\n", "C 6.153153 31.631057 -0.238233\n", "C 7.586010 31.695401 -0.179773\n", "C 8.390724 32.047572 -1.247553\n", "H 7.903308 32.291586 -2.187969\n", "C 12.973334 31.849872 0.283741\n", "H 13.944682 32.031190 -0.169145\n", "C 11.447158 30.518591 4.678739\n", "H 11.934423 30.277429 5.619969\n", "C 6.864795 30.711643 3.146118\n", "H 5.893357 30.532078 3.599511\n", "H 12.800139 32.636412 -2.439296\n", "H 10.224017 32.743662 -3.301293\n", "H 15.131785 31.441247 2.076257\n", "H 14.273933 30.694315 4.546802\n", "H 9.612512 29.848040 6.739754\n", "H 7.036117 29.960530 5.879248\n", "H 4.707408 31.099933 1.347803\n", "H 5.564992 31.851940 -1.121294\n", "N 9.666041 36.091609 3.085945\n", "O 9.598728 37.226756 3.411299\n", "\"\"\"\n", "\n", "str_geometries = {\n", " \"1.75\": geometry_1_75,\n", " \"2.00\": geometry_2_00,\n", " \"5.00\": geometry_5_00,\n", "}\n", "\n", "\n", "hivqe_result = {}" ] }, { "cell_type": "markdown", "id": "5ed92d44", "metadata": {}, "source": [ "\n", "\n", "### Étapes 2 et 3 : Optimiser le problème pour l'exécution sur du matériel quantique et l'exécuter à l'aide de la fonction Chimie d' HiVQE\n", "\n", "En fonction de la configuration de HiVQE et des géométries, obtenez des résultats de manière séquentielle.\n", "\n" ] }, { "cell_type": "markdown", "id": "316f26d8", "metadata": {}, "source": [ "\n", "\n", "#### Soumettre le calcul d(Fe-N) = 1.75 $\\AA$.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "5e103316", "metadata": {}, "outputs": [], "source": [ "hivqe_run_1_75 = hivqe.run(\n", " geometry=str_geometries[\"1.75\"],\n", " backend_name=\"\",\n", " max_states=400000000,\n", " max_expansion_states=100,\n", " molecule_options=molecule_options,\n", " hivqe_options=hivqe_options,\n", ")\n", "info_jobid_1_75 = hivqe_run_1_75.job_id" ] }, { "cell_type": "markdown", "id": "49a1ff1c", "metadata": {}, "source": [ "Suivez le travail et récupérez le résultat du calcul de d(Fe-N) = 1.75 $\\AA$.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "2e1b231f", "metadata": {}, "outputs": [], "source": [ "submitted_job_1_75 = catalog.get_job_by_id(info_jobid_1_75)\n", "stat = submitted_job_1_75.status()\n", "print(submitted_job_1_75.job_id, stat)\n", "if stat == \"DONE\":\n", " hivqe_run_1_75_energy = submitted_job_1_75.result()[\"energy\"]\n", " print(f\"Completed HiVQE calculation, Energy {hivqe_run_1_75_energy}\")\n", " hivqe_result[\"1.75\"] = hivqe_run_1_75_energy" ] }, { "cell_type": "markdown", "id": "b30a0145", "metadata": {}, "source": [ "\n", "\n", "#### Soumettre le calcul d(Fe-N) = 2.00 $\\AA$.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "66eaaa38", "metadata": {}, "outputs": [], "source": [ "hivqe_run_2_00 = hivqe.run(\n", " geometry=str_geometries[\"2.00\"],\n", " backend_name=\"\",\n", " max_states=400000000,\n", " max_expansion_states=100,\n", " molecule_options=molecule_options,\n", " hivqe_options=hivqe_options,\n", ")\n", "info_jobid_2_00 = hivqe_run_2_00.job_id" ] }, { "cell_type": "markdown", "id": "1cce86be", "metadata": {}, "source": [ "Suivez le travail et récupérez le résultat du calcul de d(Fe-N) = 2.00 $\\AA$.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "7a1ea72d", "metadata": {}, "outputs": [], "source": [ "submitted_job_2_00 = catalog.get_job_by_id(info_jobid_2_00)\n", "stat = submitted_job_2_00.status()\n", "print(submitted_job_2_00.job_id, stat)\n", "if stat == \"DONE\":\n", " hivqe_run_2_00_energy = submitted_job_2_00.result()[\"energy\"]\n", " print(f\"Completed HiVQE calculation, Energy {hivqe_run_2_00_energy}\")\n", " hivqe_result[\"2.00\"] = hivqe_run_2_00_energy" ] }, { "cell_type": "markdown", "id": "ec081701", "metadata": {}, "source": [ "\n", "\n", "#### Soumettre le calcul d(Fe-N) = 5.00 $\\AA$.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "e72c0730", "metadata": {}, "outputs": [], "source": [ "hivqe_run_5_00 = hivqe.run(\n", " geometry=str_geometries[\"5.00\"],\n", " backend_name=\"\",\n", " max_states=400000000,\n", " max_expansion_states=100,\n", " molecule_options=molecule_options,\n", " hivqe_options=hivqe_options,\n", ")\n", "info_jobid_5_00 = hivqe_run_5_00.job_id" ] }, { "cell_type": "markdown", "id": "1b0bcce6", "metadata": {}, "source": [ "Suivez le travail et récupérez le résultat du calcul de d(Fe-N) = 5.00 $\\AA$.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "2758dfba", "metadata": {}, "outputs": [], "source": [ "submitted_job_5_00 = catalog.get_job_by_id(info_jobid_5_00)\n", "stat = submitted_job_5_00.status()\n", "print(submitted_job_5_00.job_id, stat)\n", "if stat == \"DONE\":\n", " hivqe_run_5_00_energy = submitted_job_5_00.result()[\"energy\"]\n", " print(f\"Completed HiVQE calculation, Energy {hivqe_run_5_00_energy}\")\n", " hivqe_result[\"5.00\"] = hivqe_run_5_00_energy" ] }, { "cell_type": "code", "execution_count": null, "id": "a7793eea", "metadata": {}, "outputs": [], "source": [ "hivqe_result = {\n", " \"1.75\": -2373.681781,\n", " \"2.00\": -2373.694128,\n", " \"5.00\": -2373.637807,\n", "}" ] }, { "cell_type": "markdown", "id": "e6c08de7", "metadata": {}, "source": [ "\n", "\n", "### Étape 4 : Post-traitement et comparaison avec les méthodes classiques\n", "\n", "Les résultats des calculs de référence classiques (CASCI-DMRG, maxM=800 ) sont fournis pour ( 22o,22e ) afin de valider les résultats de HiVQE.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "62937d55", "metadata": {}, "outputs": [], "source": [ "rhf_result = {\n", " \"1.75\": -2373.59331683504,\n", " \"2.00\": -2373.60640773065,\n", " \"5.00\": -2373.50214278007,\n", "}\n", "casci_result = {\"1.75\": -2373.6827, \"2.00\": -2373.6948, \"5.00\": -2373.6393}" ] }, { "cell_type": "code", "execution_count": null, "id": "49a9bf81", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [] }, { "data": { "text/plain": [ "\"Output" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 1)\n", "hf_energy = [v for key, v in rhf_result.items()]\n", "casci_energy = [v for key, v in casci_result.items()]\n", "hivqe_energy = [v for key, v in hivqe_result.items()]\n", "distance_ref = [float(key) for key, v in rhf_result.items()]\n", "distance = [float(key) for key, v in hivqe_result.items()]\n", "\n", "ax.plot(distance_ref, hf_energy, \"-o\", label=\"RHF\", c=\"blue\")\n", "ax.plot(distance_ref, casci_energy, \"-o\", label=\"CASCI\", c=\"green\")\n", "ax.plot(distance, hivqe_energy, \"x\", label=\"HiVQE\", c=\"red\", markersize=20)\n", "ax.legend(fontsize=20)\n", "ax.tick_params(\"both\", labelsize=16)\n", "ax.set_xlabel(\"Fe-N bond distance ($\\AA$)\", size=20)\n", "ax.set_ylabel(\"Energy (Ha)\", size=20)\n", "ax.set_title(\"FeP-NO PES curve\", size=20)\n", "fig.set_size_inches(14, 8)" ] }, { "cell_type": "markdown", "id": "a5124358", "metadata": {}, "source": [ "\n", "\n", "## Enquête tutorielle\n", "\n", "Veuillez répondre à cette courte enquête pour nous faire part de vos commentaires sur ce didacticiel. Vos commentaires nous aideront à améliorer notre offre de contenu et l'expérience des utilisateurs.\n", "\n", "[Lien vers l'enquête](https://your.feedback.ibm.com/jfe/form/SV_9AizRyKFHEGzVs2)\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "id": "a1b8767d", "source": "© IBM Corp., 2017-2026" } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3" }, "hours": 1.5, "qpuSeconds": 300 }, "nbformat": 4, "nbformat_minor": 5 }