{ "cells": [ { "cell_type": "markdown", "id": "b6d1e3ec", "metadata": {}, "source": [ "---\n", "title: \"Curvas de disociación PES con Qunova HiVQE\"\n", "description: \"Utilice Qunova HiVQE para calcular las curvas de la superficie de energía potencial de disociación (PES).\"\n", "---\n", "\n", "{/* cspell:ignore fontsize labelsize DMRG hivqe mcscf CASCI */}\n", "\n", "\n", "\n", "# Curvas de disociación PES con Qunova HiVQE\n", "\n" ] }, { "attachments": {}, "cell_type": "markdown", "id": "a6f69b77", "metadata": {}, "source": [ "\n", " Las funciones Qiskit son una función experimental disponible únicamente para los usuarios de los planes IBM Quantum® Premium Plan, Flex Plan y On-Prem (a través de IBM Quantum Platform API). Se trata de versiones preliminares sujetas a cambios.\n", "\n", "\n", "*Estimación de uso (NOTA: Esto es sólo una estimación. Su tiempo de ejecución puede variar)*\n", "\n", "* Li2S: Cinco minutos de QPU en un procesador Heron r2\n", "* FeP-NO: Cinco minutos de QPU en un procesador Heron r2\n", "\n" ] }, { "cell_type": "markdown", "id": "8bf80006", "metadata": {}, "source": [ "\n", "\n", "## En segundo plano\n", "\n", "Calcular con precisión las energías de las reacciones químicas es crucial para los avances científicos en ciencia de materiales, ingeniería química, descubrimiento de fármacos y otros campos. Entre los diversos sistemas químicos, el sistema Li-S ha suscitado un gran interés para comprender y desarrollar nuevas composiciones de baterías. Este tutorial proporciona experiencia práctica en el cálculo de la superficie de energía potencial (PES) de disociación del enlace Li-S de un sistema $Li_2S$ eliminando un átomo de litio mediante cálculos HiVQE. Los resultados pueden compararse con cálculos de referencia (CASCI), así como con métodos clásicos como Hartree-Fock (HF) para un problema de 20 qubits.\n", "\n" ] }, { "cell_type": "markdown", "id": "55b94021", "metadata": {}, "source": [ "\n", "\n", "## Requisitos\n", "\n", "Instale las siguientes dependencias para ejecutar el código de este tutorial.\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", "## Configuración\n", "\n", "Para ejecutar este tutorial, importe la función `qunova/hivqe-chemistry` a través de `QiskitFunctionCatalog`. Para ejecutar esta función, necesita una cuenta del plan IBM Quantum Premium, Flex o On-Prem ( IBM Quantum Platform API) con una licencia de Qunova.\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", "## Parte 1: Li2S ( 20Q )\n", "\n", "\n", "\n", "### Paso 1: Asignar entradas clásicas a un problema cuántico\n", "\n", "Definir $Li_2S$ geometrías en formato diccionario para diferentes distancias de enlace de Li-S para calcular la curva PES. Estas geometrías se optimizan mediante cálculos en 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 los cálculos se realizarán con las opciones definidas a continuación. Utilizando la base sto3g para $Li_2S$, hay 19 orbitales espaciales con 22 electrones. Para ejecutar el caso ( 10o,10e ) con el cálculo HiVQE, puede definir 10 orbitales activos y seis orbitales congelados. En cada iteración, se utilizarán 100 disparos para muestrear la configuración de electrones generada por el circuito cuántico ExcitationPreserving (`epa`) con entrelazamiento `circular` y dos repeticiones (`reps`). El número máximo de iteraciones se fija en 30 para garantizar la terminación de la iteración con convergencia energética.\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", "### Pasos 2 y 3: Optimizar el problema para su ejecución en hardware cuántico y ejecutarlo utilizando la función Chemistry de HiVQE\n", "\n", "Configure el bucle `for` para ejecutar los cálculos de HiVQE con geometrías con las opciones definidas a continuación. Los trabajos se envían en el bucle `for` . En este tutorial, enviará seis geometrías y obtendrá los resultados cuando todas estén terminadas. En la ejecución de la función principal, es necesario definir `max_states` y `max_expansion_states` para controlar el tamaño máximo de la matriz del subespacio y para controlar cuántos estados se pueden generar utilizando los métodos clásicos de expansión CI por iteración. Los identificadores de trabajo de la función se almacenarán en el diccionario con cada etiqueta de geometría para seguir el rastro y procesar la salida.\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": [ "Comprobemos si todos los trabajos siguen en ejecución o se han completado.\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": [ "Una vez completados todos los trabajos, recuperemos todos los resultados de los cálculos.\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": [ "Se puede hacer un seguimiento de todo el tiempo de ejecución de QPU utilizado en el trabajo iniciando sesión en [IBM Quantum Platform](/) y viendo los trabajos enviados con la etiqueta `qunova-chemistry-hivqe` .\n", "\n" ] }, { "cell_type": "markdown", "id": "75fc5cf6", "metadata": {}, "source": [ "\n", "\n", "### Paso 4: Posprocesamiento y comparación con métodos clásicos\n", "\n", "El cálculo de referencia clásico (CASCI) puede realizarse para ( 10o,10e ) para validar los resultados 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", "### Trazado de la curva de disociación para un Li\\_2S\n", "\n", "Vamos a trazar y comparar los resultados de HiVQE con los de HF y CASCI.\n", "Puede observarse que todos los cálculos de HiVQE coinciden con el resultado de referencia clásico (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", "## Parte 2: FeP-NO ( 44Q )\n", "\n", "Tenga en cuenta que para ejecutar la función en el sistema FeP-NO con la configuración que se muestra en este ejemplo, necesita una licencia que permita utilizar la función con al menos 44 qubits. Envíe un correo electrónico a [qiskit.support @ qunovacomputing.com](mailto:qiskit.support@qunovacomputing.com) para solicitar información sobre cómo obtener una licencia.\n", "\n", "\n", "\n", "### Paso 1: Asignar entradas clásicas a un problema cuántico\n", "\n", "Defina las opciones para los cálculos de 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": [ "Definir FeP-NO geometrías en formato diccionario para diferentes distancias de enlace de Fe-N para calcular la curva 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 0.283741\\nH 13.944682 32.031190 -0.169145\\nC 11.447158 30.518591 4.678739\\nH 11.934423 30.277429 5.619969\\nC 6.864795 30.711643 3.146118\\nH 5.893357 30.532078 3.599511\\nH 12.800139 32.636412 -2.439296\\nH 10.224017 32.743662 -3.301293\\nH 15.131785 31.441247 2.076257\\nH 14.273933 30.694315 4.546802\\nH 9.612512 29.848040 6.739754\\nH 7.036117 29.960530 5.879248\\nH 4.707408 31.099933 1.347803\\nH 5.564992 31.851940 -1.121294\\nN 9.666041 36.091609 3.085945\\nO 9.598728 37.226756 3.411299\\n'}" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "geometry_1_75 = \"\"\"\n", "Fe 9.910596 31.534095 1.798088\n", "N 10.557481 31.888419 -0.055204\n", "N 11.823496 31.255002 2.384659\n", "N 9.292831 30.783362 3.568730\n", "N 8.036805 31.418327 1.124265\n", "C 9.784765 32.177349 -1.158798\n", "C 10.612656 32.501029 -2.296868\n", "C 11.903375 32.404043 -1.876832\n", "C 11.859093 32.028943 -0.483750\n", "C 12.965737 31.464698 1.641427\n", "C 14.146517 31.236323 2.440231\n", "C 13.713061 30.885870 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"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 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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 31.849872 0.283741\\nH 13.944682 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"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", "### Pasos 2 y 3: Optimizar el problema para su ejecución en hardware cuántico y ejecutarlo utilizando la función Chemistry de HiVQE\n", "\n", "A partir de la configuración de HiVQE y las geometrías, obtener resultados secuencialmente.\n", "\n" ] }, { "cell_type": "markdown", "id": "316f26d8", "metadata": {}, "source": [ "\n", "\n", "#### Enviar d(Fe-N) = 1.75 $\\AA$ cálculo.\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": [ "Rastrea el trabajo y recupera el resultado para el cálculo 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", "#### Enviar d(Fe-N) = 2.00 $\\AA$ cálculo.\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": [ "Rastrea el trabajo y recupera el resultado para el cálculo 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", "#### Enviar d(Fe-N) = 5.00 $\\AA$ cálculo.\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": [ "Rastrea el trabajo y recupera el resultado para el cálculo 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", "### Paso 4: Posprocesamiento y comparación con métodos clásicos\n", "\n", "Se facilitan los resultados del cálculo clásico de referencia (CASCI-DMRG, maxM=800 ) para ( 22o,22e ) validar los resultados 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", "## Encuesta tutorial\n", "\n", "Responda a esta breve encuesta para darnos su opinión sobre este tutorial. Su opinión nos ayudará a mejorar nuestra oferta de contenidos y la experiencia de los usuarios.\n", "\n", "[Enlace a la encuesta](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 }