{
"cells": [
{
"cell_type": "markdown",
"id": "1682996d",
"metadata": {},
"source": [
"---\n",
"title: \"Cálculos de disolvente implícito con « Qiskit Serverless »\"\n",
"description: \"Aprende a calcular los efectos del disolvente implícito en hardware cuántico utilizando SQD e IEF-PCM.\"\n",
"---\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "d2c31ae8",
"metadata": {},
"source": [
"\n",
"\n",
"# Cálculos de disolvente implícito con « Qiskit Serverless »\n",
"\n",
"*Tiempo estimado de ejecución: 2 minutos en un procesador Heron r2 (NOTA: Se trata únicamente de una estimación). (El tiempo de ejecución puede variar.)*\n",
"\n",
"{/* cspell:ignore avas AVAS hcore textit TRIC dmas mocore ncore ncas mocas fermilevel ecore orbts iiter IITER edup textcoords xytext fontsize fontweight frameon */}\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "8bf80006",
"metadata": {},
"source": [
"\n",
"\n",
"## Resultados del aprendizaje\n",
"\n",
"Una vez completado este tutorial, los usuarios deberían comprender:\n",
"\n",
"* Cómo configurar y ejecutar un flujo de trabajo remoto con [Qiskit Serverless](/docs/guides/serverless)\n",
"* Cómo calcular los efectos del disolvente implícito utilizando un ordenador cuántico\n",
"\n",
"\n",
"\n",
"## Requisitos previos\n",
"\n",
"Recomendamos a los usuarios que se familiaricen con el siguiente tema antes de seguir este tutorial:\n",
"\n",
"* [Diagonalización cuántica basada en muestras](/learning/courses/quantum-diagonalization-algorithms/sqd-overview)\n",
"\n",
"\n",
"\n",
"## En segundo plano\n",
"\n",
"Los cálculos con disolvente implícito se utilizan con frecuencia en biofísica computacional. Estos modelos describen cómo un compuesto soluto interactúa con un disolvente, sin modelar directamente el sistema disolvente. En su lugar, se realiza una aproximación en la que el modelo del sistema de solutos se integra en una representación matemática de un medio dieléctrico caracterizado empíricamente. Esta aproximación dieléctrica interactúa a continuación con el soluto, que a su vez se modela directamente. El medio dieléctrico influye en las características del sistema de solutos, como su energía de estado fundamental, al interactuar con su campo electrónico. Esto es importante para los modelos biofísicos que se utilizan, por ejemplo, en el descubrimiento de fármacos, ya que los compuestos se comportan de manera diferente en distintos entornos dieléctricos. El modelado de un compuesto en el aire (en vacío) dará lugar a un comportamiento diferente al de modelarlo en agua. Dado que los compuestos farmacéuticos deben introducirse en el cuerpo humano, que a su vez está compuesto principalmente por agua, resulta útil simular el comportamiento de un compuesto en una solución como el agua, en lugar de hacerlo en el vacío. Con los modelos de disolvente implícito, podemos conseguir este comportamiento de forma económica, aunque el resultado final suele ser más aproximado que el de su contrapartida: los modelos de disolvente explícito, que requieren un mayor esfuerzo computacional y crean representaciones directas tanto de las moléculas del soluto como de las del disolvente.\n",
"\n",
"En este tutorial, mostramos cómo se puede integrar un algoritmo cuántico, la diagonalización cuántica basada en muestras (SQD), en un modelo de disolvente implícito relativamente poco exigente desde el punto de vista computacional. En el ejemplo, describimos cómo se comporta la metilamina cuando se disuelve en agua. Comparamos el algoritmo cuántico con un método clásico de referencia denominado CASCI y demostramos que los resultados de ambos cálculos coinciden en gran medida. Presentamos una arquitectura de supercomputación centrada en la cuántica a escala reducida, que traslada el costoso procesamiento clásico posterior de la parte de muestreo cuántico de la rutina a un entorno basado en la nube dentro de Qiskit Serverless. El código también muestra cómo se lleva a cabo la paralelización entre los núcleos de CPU disponibles de forma remota para reducir el tiempo de cálculo.\n",
"\n",
"Qiskit Serverless es un marco para ejecutar cargas de trabajo cuánticas y clásicas distribuidas sin necesidad de gestionar la infraestructura. No hay aprovisionamiento de servidores (no se ponen en marcha EC2s, clústeres, Docker contenedores), ni herramientas de orquestación ( Kubernetes, Docker Swarm), ni tareas de supervisión o mantenimiento. Cada tarea sin servidor se ejecuta en un contenedor limpio, ejecuta tu código y, a continuación, se cierra. No hay memoria entre tareas. Solo tienes que escribir el código y enviar el trabajo. Dentro de una tarea sin servidor, un programa puede acceder sin problemas a los backends de IBM Quantum® y procesar los resultados posteriormente de la forma habitual. Con « Qiskit Serverless », los usuarios pueden acceder a núcleos de CPU y memoria remotos que están siempre activos, lo que permite distribuir determinadas cargas de trabajo clásicas entre recursos remotos. Los usuarios también obtienen ciertas ventajas en el procesamiento paralelo de programas, al tiempo que evitan los habituales problemas derivados del apagado del dispositivo durante la ejecución. Para obtener más información sobre Qiskit Serverless, consulta su [documentación](/docs/guides/serverless), así como el material adicional disponible en [GitHub](https://qiskit.github.io/qiskit-serverless/index.html).\n",
"\n",
"Este tutorial muestra una aplicación relevante de lo siguiente:\n",
"\n",
"* Diagonalización cuántica basada en muestras\n",
"* Modelos computacionales cliente-servidor para la computación cuántica\n",
"\n",
"Este tutorial se inspira y se basa en una investigación llevada a cabo en la Clínica Cleveland, tal y como se describe en [Kaliakin, Danil, et al., «Implicit solvent sample-based quantum diagonalization» The Journal of Physical Chemistry B 129.23 (2025): 5788-5796](https://pubs.acs.org/doi/10.1021/acs.jpcb.5c01030), en el que se expone el flujo de trabajo completo para los cálculos de disolvente implícito y se amplía con la autoconsistencia iterativa del disolvente («The Heartwood Algorithm», M. Motta, T. Pellegrini (2025), optimización geométrica y selección automática de la disposición de los qubits. Consulte la [plantilla](/docs/guides/function-template-chemistry-workflow) «SQD IEF-PCM Qiskit Function», desarrollada conjuntamente por la Cleveland Clinic y IBM® a partir de la investigación de la Cleveland Clinic, para disponer de una interfaz simplificada de tipo «caja negra» que permita realizar cálculos con disolvente implícito.\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "55b94021",
"metadata": {},
"source": [
"\n",
"\n",
"## Requisitos\n",
"\n",
"Antes de comenzar este tutorial, asegúrate de tener instalado lo siguiente:\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "9456ea67",
"metadata": {},
"source": [
"* Qiskit SDK v2.0 o posterior, con soporte [para visualización](/docs/api/qiskit/visualization)\n",
"* Qiskit Runtime v0.40 o posterior (`pip install qiskit-ibm-runtime`)\n",
"* Qiskit SDK v2.0 o posterior, con soporte para visualización `pip install qiskit[visualization]`\n",
"* Qiskit Runtime v0.40 o posterior `pip install qiskit_ibm_runtime`\n",
"* Catálogo de Qiskit « IBM » `pip install qiskit_ibm_catalog`\n",
"* Qiskit IBM : sin servidor `pip install qiskit_serverless`\n",
"* Complemento de Qiskit: Diagonalización cuántica basada en muestras (SQD) v0.12.0 `pip install qiskit_addon_sqd`\n",
"* PySCF `pip install pyscf`\n",
"* FFSIM `pip install ffsim`\n",
"* Matplotlib `pip install matplotlib`\n",
"* Geométrico `pip install geometric`\n",
"\n"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "7db2e559",
"metadata": {},
"source": [
"\n",
"\n",
"## Configuración\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2c88b910",
"metadata": {},
"outputs": [],
"source": [
"# Establish Quantum Resource connection\n",
"from qiskit_ibm_runtime import QiskitRuntimeService\n",
"\n",
"service = QiskitRuntimeService()\n",
"\n",
"backend = service.least_busy()\n",
"print(f\"Using backend {backend.name}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "af9286cf",
"metadata": {},
"outputs": [],
"source": [
"# Establish Classical HPC Resource connection\n",
"from qiskit_ibm_catalog import QiskitFunction, QiskitServerless\n",
"\n",
"client = QiskitServerless()"
]
},
{
"cell_type": "markdown",
"id": "aee647af",
"metadata": {},
"source": [
"Crea un directorio justo al lado del programa principal del cuaderno llamado `source_files`. En este directorio deberá colocar los archivos de « Python » que desee compartir con el entorno informático remoto. Debes crear dos archivos:\n",
"\n",
"* `source_files\\diagonalization_engine.py`\n",
"* `source_files\\classical_simulation.py`\n",
"\n",
"Haz clic para ampliar el texto de cada script que aparece a continuación y, a continuación, copia y pega el contenido en un archivo local con estas rutas.\n",
"\n",
"\n",
" \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
"\n",
"\n",
"\n",
" Para obtener más información, consulta la [guía de plantillas de funciones de Qiskit de SQD IEF-PCM](/docs/guides/function-template-chemistry-workflow) (desarrollada conjuntamente por la Clínica Cleveland y IBM ) a la que se hace referencia anteriormente. Véase también la [`qiskit_addon_sqd`](/docs/addons/qiskit-addon-sqd) biblioteca.\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7347c7e5",
"metadata": {
"tags": [
"id-diagonalization"
]
},
"outputs": [],
"source": [
"#!/usr/bin/env python3\n",
"import numpy as np\n",
"from json.encoder import JSONEncoder\n",
"from json.decoder import JSONDecoder\n",
"from functools import partial\n",
"import os\n",
"\n",
"from qiskit_ibm_runtime import QiskitRuntimeService\n",
"from qiskit_serverless import distribute_task, get_arguments, get, save_result\n",
"from qiskit_addon_sqd.fermion import (\n",
" SCIResult,\n",
" diagonalize_fermionic_hamiltonian,\n",
" solve_sci,\n",
")\n",
"\n",
"\n",
"### Argument retrieval\n",
"args = get_arguments()\n",
"\n",
"data = args[\"data\"] # Chemistry Data\n",
"energy_tol = args[\"energy_tol\"] # SQD option\n",
"occupancies_tol = args[\"occupancies_tol\"] # SQD option\n",
"max_iterations = args[\"max_iterations\"] # SQD option\n",
"symmetrize_spin = args[\"symmetrize_spin\"] # Eigenstate solver option\n",
"carryover_threshold = args[\"carryover_threshold\"] # Eigenstate solver option\n",
"num_batches = args[\"num_batches\"] # Eigenstate solver option\n",
"samples_per_batch = args[\"samples_per_batch\"] # Eigenstate solver option\n",
"max_cycle = args[\"max_cycle\"] # Eigenstate solver option\n",
"mem = args[\"mem\"] # Memory per Worker\n",
"\n",
"\n",
"# --- fan‑out target: 1 CPU + mem GB RAM per call -------------\n",
"@distribute_task(target={\"cpu\": 1, \"mem\": mem * 1024**3})\n",
"def _solve_sci_worker(\n",
" ix, ci_strs, one_body_tensor, two_body_tensor, norb, nelec, spin_sq\n",
"):\n",
" print(f\">>>>> WORKER {ix} INITIATED\")\n",
" res = solve_sci(\n",
" ci_strs,\n",
" one_body_tensor,\n",
" two_body_tensor,\n",
" norb=norb,\n",
" nelec=nelec,\n",
" spin_sq=spin_sq,\n",
" )\n",
"\n",
" print(f\">>>>> WORKER {ix} COMPLETE\")\n",
" return res\n",
"\n",
"\n",
"def distribute_solve_sci_batch(\n",
" ci_strings: list[tuple[np.ndarray, np.ndarray]],\n",
" one_body_tensor: np.ndarray,\n",
" two_body_tensor: np.ndarray,\n",
" norb: int,\n",
" nelec: tuple[int, int],\n",
" *,\n",
" spin_sq: float | None = None,\n",
" **kwargs,\n",
") -> list[SCIResult]:\n",
" \"\"\"Diagonalize Hamiltonian in subspaces, parallelizing across\n",
" vCPUs in the Serverless environment.\n",
"\n",
" Args:\n",
" ci_strings: List of pairs (strings_a, strings_b) of arrays of\n",
" spin-alpha CI strings and spin-beta CI strings whose Cartesian\n",
" product gives the basis of the subspace in which to perform a\n",
" diagonalization.\n",
" one_body_tensor: The one-body tensor of the Hamiltonian.\n",
" two_body_tensor: The two-body tensor of the Hamiltonian.\n",
" norb: The number of spatial orbitals.\n",
" nelec: The numbers of alpha and beta electrons.\n",
" spin_sq: Target value for the total spin squared for the ground state.\n",
" If ``None``, no spin will be imposed.\n",
" **kwargs: Keyword arguments to pass to\n",
" `pyscf.fci.selected_ci.kernel_fixed_space`\n",
" (https://pyscf.org/pyscf_api_docs/pyscf.fci.html#pyscf.fci.selected_ci.kernel_fixed_space\n",
"\n",
" Returns:\n",
" The results of the diagonalizations in the subspaces given by ci_strings.\n",
" \"\"\"\n",
" inputs = [\n",
" (ix, ci_strs, one_body_tensor, two_body_tensor, norb, nelec, spin_sq)\n",
" for ix, ci_strs in enumerate(ci_strings)\n",
" ]\n",
"\n",
" # fan‑out: spawn one worker per input tuple\n",
" print(\">>>>> ENTERING WORKER FAN-OUT\")\n",
" refs = [_solve_sci_worker(*input_) for input_ in inputs]\n",
" print(\">>>>> WAITING ON WORKERS TO FINISH TASKS\")\n",
"\n",
" # fan‑in: block until every worker finishes\n",
" results = get(refs)\n",
" print(\">>>>> DISTRIBUTED JOBS COMPLETED\")\n",
"\n",
" return results\n",
"\n",
"\n",
"# A caveat of executing a Python program remotely is\n",
"# that the inputs to the remote program must be passed\n",
"# over an internet network. Similarly, the outputs\n",
"# must be passed back to the local program via the same\n",
"# structure. Python objects are not always able to be\n",
"# passed over a network, and must be encoded in a\n",
"# JSON serializable format.\n",
"i_data = JSONDecoder().decode(data)\n",
"\n",
"# i_data has all of the information needed from the\n",
"# local program to pick up where the computation left off\n",
"# after its submission to the remote environment.\n",
"[\n",
" job_id,\n",
" hcore,\n",
" eri,\n",
" num_orbitals,\n",
" nuclear_repulsion_energy,\n",
" num_elec_a,\n",
" num_elec_b,\n",
"] = i_data\n",
"\n",
"# Re-convert data back into numpy format, after serialization\n",
"hcore = np.array(hcore)\n",
"eri = np.array(eri)\n",
"nuclear_repulsion_energy = np.float64(nuclear_repulsion_energy)\n",
"\n",
"# Instantiate Runtime Service to retrieve the\n",
"# bitstrings from the QPU job. We provided these\n",
"# credentials upon Serverless setup.\n",
"service = QiskitRuntimeService(\n",
" channel=os.environ.get(\"QISKIT_IBM_CHANNEL\"),\n",
" token=os.environ.get(\"QISKIT_IBM_TOKEN\"),\n",
" instance=os.environ.get(\"QISKIT_IBM_INSTANCE\"),\n",
")\n",
"\n",
"# retrieving the QPU job data from the Serverless side\n",
"job = service.job(job_id)\n",
"primitive_result = job.result()\n",
"pub_result = primitive_result[0]\n",
"bit_array = pub_result.data.meas # Getting the bitstrings\n",
"\n",
"# Pass options to the built-in eigensolver\n",
"sci_solver = partial(\n",
" distribute_solve_sci_batch, spin_sq=0.0, max_cycle=max_cycle\n",
")\n",
"\n",
"# List to capture intermediate results\n",
"result_history = []\n",
"\n",
"\n",
"def callback(results: list[SCIResult]):\n",
" result_history.append(results)\n",
" iteration = len(result_history)\n",
" print(f\">>>>> SQD ITERATION {iteration}\")\n",
" for i, result in enumerate(results):\n",
" print(f\">>>>> SUBSAMPLE {i}\")\n",
" print(f\">>>>> \\tENERGY: {result.energy + nuclear_repulsion_energy}\")\n",
" print(\n",
" f\">>>>> \\tSUBSPACE DIMENSION: {np.prod(result.sci_state.amplitudes.shape)}\"\n",
" )\n",
"\n",
"\n",
"result = diagonalize_fermionic_hamiltonian(\n",
" hcore,\n",
" eri,\n",
" bit_array,\n",
" samples_per_batch=samples_per_batch,\n",
" norb=num_orbitals,\n",
" nelec=(num_elec_a, num_elec_b),\n",
" num_batches=num_batches,\n",
" energy_tol=energy_tol,\n",
" occupancies_tol=occupancies_tol,\n",
" max_iterations=max_iterations,\n",
" sci_solver=sci_solver,\n",
" symmetrize_spin=symmetrize_spin,\n",
" carryover_threshold=carryover_threshold,\n",
" callback=callback,\n",
" seed=12345,\n",
")\n",
"\n",
"print(\">>>>> EXACT DIAGONALIZATION COMPLETE. CLEANING UP, SERIALIZING DATA.\")\n",
"# Numpy arrays are not JSON serializable.\n",
"# Convert them to List objects before using the JSONEncoder\n",
"o_data = JSONEncoder().encode(\n",
" [\n",
" result.energy + nuclear_repulsion_energy,\n",
" result.energy,\n",
" result.rdm1.tolist(),\n",
" result.rdm2.tolist(),\n",
" [x.tolist() for x in result.orbital_occupancies],\n",
" [\n",
" result.sci_state.nelec,\n",
" result.sci_state.norb,\n",
" [x.tolist() for x in result.sci_state.orbital_occupancies()],\n",
" [x.tolist() for x in result.sci_state.rdm()],\n",
" ],\n",
" ]\n",
")\n",
"\n",
"# JSON-safe package\n",
"save_result({\"outputs\": o_data}) # single JSON blob returned to client"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "41807b3a",
"metadata": {
"tags": [
"id-classical"
]
},
"outputs": [],
"source": [
"#!/usr/bin/env python3\n",
"from json.encoder import JSONEncoder\n",
"from json.decoder import JSONDecoder\n",
"\n",
"from qiskit_serverless import get_arguments, save_result\n",
"\n",
"import pyscf\n",
"from pyscf import gto, scf\n",
"from pyscf.solvent import pcm\n",
"from pyscf.mcscf import avas\n",
"\n",
"import psutil\n",
"\n",
"mem_info = (\n",
" psutil.virtual_memory()\n",
") # Get information about virtual memory (RAM)\n",
"total_ram_gb = mem_info.total / (1024**3) # Convert bytes to GB\n",
"print(f\">>>>> SERVERLESS TOTAL RAM: {total_ram_gb:.2f} GB\")\n",
"\n",
"### Argument retrieval\n",
"args = get_arguments()\n",
"data = args[\"data\"] # Chemistry Data\n",
"\n",
"i_data = JSONDecoder().decode(data)\n",
"[mol_geo, eps, ao_labels] = i_data\n",
"\n",
"print(\">>>>> DEFINING MOLECULE\")\n",
"mol = gto.M()\n",
"mol.atom = mol_geo\n",
"mol.basis = \"cc-pVDZ\"\n",
"mol.unit = \"Ang\"\n",
"mol.charge = 0\n",
"mol.spin = 0\n",
"mol.verbose = 0\n",
"\n",
"print(\">>>>> BUILDING MOLECULE\")\n",
"mol.build()\n",
"\n",
"print(\">>>>> DEFINING PCM\")\n",
"cm = pcm.PCM(mol)\n",
"cm.eps = eps # for water\n",
"cm.method = \"IEF-PCM\"\n",
"\n",
"print(\">>>>> BUILDING RESTRICTED HARTREE FOCK\")\n",
"mf = scf.RHF(mol).PCM(cm) # This is the Final SCF object\n",
"mf.kernel(verbose=0)\n",
"\n",
"print(\">>>>> RUNNING AVAS\")\n",
"avas_ = avas.AVAS(mf, ao_labels, with_iao=True, canonicalize=True, verbose=0)\n",
"avas_.kernel()\n",
"norb, ne_act, mo_avas = avas_.ncas, avas_.nelecas, avas_.mo_coeff\n",
"\n",
"print(\">>>>> STARTING CASCI\")\n",
"mc_pcm = pyscf.mcscf.CASCI(mf, norb, ne_act).PCM(\n",
" cm\n",
") # Make sure to decorate the CASCI object with PCM\n",
"mc_pcm.mo_coeff = mo_avas\n",
"# mc_pcm.max_memory = 140000\n",
"\n",
"(CASCI_E, _, _, _, _) = mc_pcm.kernel(verbose=0)\n",
"\n",
"print(f\">>>>> CASCI_E: {CASCI_E}\")\n",
"o_data = JSONEncoder().encode([float(CASCI_E)])\n",
"\n",
"# JSON-safe package\n",
"save_result({\"outputs\": o_data}) # single JSON blob returned to client"
]
},
{
"cell_type": "markdown",
"id": "c94aebd8",
"metadata": {},
"source": [
"Tenemos que compartir el programa destinado a ejecutarse en el entorno en la nube y volver a subirlo cada vez que modifiquemos su código fuente:\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cc00669f",
"metadata": {},
"outputs": [],
"source": [
"client.upload(\n",
" QiskitFunction(\n",
" title=\"diagonalization_engine\",\n",
" entrypoint=\"diagonalization_engine.py\", # lives in ./source_files\n",
" working_dir=\"source_files\",\n",
" )\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ef726eee",
"metadata": {},
"outputs": [],
"source": [
"client.upload(\n",
" QiskitFunction(\n",
" title=\"classical_simulation\",\n",
" entrypoint=\"classical_simulation.py\", # lives in ./source_files\n",
" working_dir=\"source_files\",\n",
" )\n",
")"
]
},
{
"cell_type": "markdown",
"id": "0cd9185e",
"metadata": {},
"source": [
"\n",
"\n",
"## Ejemplo de simulador a pequeña escala\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "b0db693d",
"metadata": {},
"source": [
"Este tutorial no utiliza un simulador a pequeña escala, ya que su objetivo es mostrar una aplicación cuántica escalable que va más allá del ámbito de la exploración mediante simuladores. En cambio, más adelante mostramos cómo se puede implementar este método utilizando un método de comparación clásico de vanguardia denominado CASCI.\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "431a5bd2-e6ed-471b-ad9e-c4edd27784a8",
"metadata": {},
"source": [
"\n",
"\n",
"## Ejemplo de hardware a gran escala\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5df67bda",
"metadata": {},
"outputs": [],
"source": [
"# This is a useful helper function that displays\n",
"# remote job execution details to the user's local machine\n",
"def feedback_serverless(serverless_job):\n",
" import time\n",
"\n",
" # Wait for the job to execute\n",
" print(f\">>>>> Serverless status: {serverless_job.job_id}\")\n",
" timer = 0\n",
" while timer < 10000:\n",
" if (\n",
" serverless_job.status() == \"QUEUED\"\n",
" or serverless_job.status() == \"INITIALIZING\"\n",
" or serverless_job.status() == \"RUNNING\"\n",
" ):\n",
" print(f\">>>>> [{timer}s] Serverless job {serverless_job.job_id}: \\\n",
" {serverless_job.status()}\")\n",
" time.sleep(10)\n",
" timer += 10\n",
"\n",
" elif serverless_job.status() == \"ERROR\":\n",
" print(\n",
" f\">>>>> Serverless job {serverless_job.job_id}: {serverless_job.status()}\"\n",
" )\n",
" print(\">>>>> Logs:\")\n",
" print(serverless_job.logs())\n",
" break\n",
"\n",
" elif serverless_job.status() == \"DONE\":\n",
" print(\n",
" f\">>>>> Serverless job {serverless_job.job_id}: {serverless_job.status()}\"\n",
" )\n",
" break\n",
"\n",
" else:\n",
" break\n",
"\n",
" return"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "988ee237",
"metadata": {},
"source": [
"\n",
"\n",
"## Paso 1: Asignar entradas clásicas a un problema cuántico\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "cf4bbd59",
"metadata": {},
"source": [
"\n",
"\n",
"### 1.1: Inicializar el objeto «molecule» utilizando la geometría molecular conocida de « $\\textit{a priori}$ »\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "566e06b4",
"metadata": {},
"outputs": [],
"source": [
"# Reference guide for building molecule structures:\n",
"# https://pyscf.org/user/gto.html\n",
"# Video tutorial on building molecular objects in PySCF:\n",
"# https://www.youtube.com/watch?v=cNC2cY9E9j0\n",
"\n",
"molecule_name = \"Methylamine\"\n",
"\n",
"methylamine_geo = \"\"\"\n",
" N -0.7154 0.0000 0.0000;\n",
" C 0.7154 0.0000 0.0000;\n",
" H 1.1069 0.0916 1.0174;\n",
" H 1.0996 0.8349 -0.5930;\n",
" H 1.0996 -0.9274 -0.4345;\n",
" H -1.0625 0.8564 0.4294;\n",
" H -1.0625 -0.7661 0.5753;\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4a7aec02",
"metadata": {},
"outputs": [],
"source": [
"# Imports\n",
"import pyscf\n",
"from pyscf import gto # Deals with molecular initialization\n",
"from pyscf import scf # Solvation methods\n",
"\n",
"# Explicitly defining the Methylamine molecule\n",
"mol = gto.M()\n",
"mol.atom = methylamine_geo\n",
"mol.basis = \"cc-pVDZ\"\n",
"mol.unit = \"Ang\"\n",
"mol.charge = 0\n",
"mol.spin = 0\n",
"mol.verbose = 0\n",
"\n",
"mol.build()"
]
},
{
"cell_type": "markdown",
"id": "99359d80",
"metadata": {},
"source": [
"\n",
"\n",
"### 1.2: Define los efectos de solvatación utilizando el modelo de continuo polarizable (PCM)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9294b528",
"metadata": {},
"outputs": [],
"source": [
"# You can explore other solvents (such as methanol) by\n",
"# retrieving other dielectric parameters from:\n",
"# https://gaussian.com/scrf/\n",
"from pyscf.solvent import pcm\n",
"\n",
"eps_water = 78.3553 # If solvating in a different medium,\n",
"# set this constant appropriately using a known value"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ffccfb92",
"metadata": {},
"outputs": [],
"source": [
"cm = pcm.PCM(mol)\n",
"cm.eps = eps_water # PySCF defaults to water solvation,\n",
"# but here we show this solvation parameter explicitly\n",
"\n",
"cm.method = (\n",
" \"IEF-PCM\" # Alternative solvation models include C-PCM, SS(V)PE, COSMO\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bfef3c9b",
"metadata": {},
"outputs": [],
"source": [
"# Create a \"Restricted Hartree-Fock\" object for the solute,\n",
"# then wrap the SCF object with a Polarizable Continuum Model\n",
"mf_pcm0 = scf.RHF(mol).PCM(\n",
" cm\n",
") # Restricted Hartree-Fock misses instantaneous correlations,\n",
"# post-HF methods like CCSD, CI, MP2 might be worth exploring"
]
},
{
"cell_type": "markdown",
"id": "b5e6c20a",
"metadata": {},
"source": [
"\n",
"\n",
"### 1.3: Optimización geométrica mediante TRIC\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "493402b3",
"metadata": {},
"outputs": [],
"source": [
"# Geometry optimization with geomeTRIC\n",
"from pyscf.geomopt.geometric_solver import (\n",
" optimize,\n",
") # GeomeTRIC under the hood, for geometry optimization\n",
"\n",
"mol_opt = optimize(\n",
" mf_pcm0, tol_grad=3e-4, verbose=0\n",
") # Use geomeTRIC/TRIC under the hood"
]
},
{
"cell_type": "markdown",
"id": "58c168bf",
"metadata": {},
"source": [
"\n",
"\n",
"### 1.4: Prepara el modelo continuo y el objeto de campo medio con las variables pertinentes\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1834cb22",
"metadata": {},
"outputs": [],
"source": [
"from pyscf.mcscf import avas\n",
"\n",
"# Re-define PCM\n",
"cm = pcm.PCM(mol_opt)\n",
"cm.eps = eps_water # for water\n",
"cm.method = \"IEF-PCM\"\n",
"\n",
"# Re-build Restricted Hartree Fock object\n",
"mf_opt = scf.RHF(mol_opt).PCM(cm)\n",
"mf_opt.kernel(verbose=0)\n",
"\n",
"# Run AVAS\n",
"ao_labels = [\"C 2s\", \"C 2p\", \"N 2s\", \"N 2p\", \"H 1s\"]\n",
"avas_ = avas.AVAS(\n",
" mf_opt, ao_labels, with_iao=True, canonicalize=True, verbose=0\n",
")\n",
"avas_.kernel()\n",
"norb, ne_act, mo_avas = avas_.ncas, avas_.nelecas, avas_.mo_coeff\n",
"\n",
"num_elec_a = (ne_act + mol_opt.spin) // 2\n",
"num_elec_b = (ne_act - mol_opt.spin) // 2"
]
},
{
"cell_type": "markdown",
"id": "ac6f36e3",
"metadata": {},
"source": [
"\n",
"\n",
"## Paso 2: Optimizar el problema para su ejecución en hardware cuántico\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "52506835",
"metadata": {},
"source": [
"Para obtener más información sobre las funciones auxiliares que se muestran aquí, consulta el tutorial «[Diagonalización cuántica basada en muestras de un hamiltoniano químico](/docs/tutorials/sample-based-quantum-diagonalization) ».\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dc27c42e",
"metadata": {},
"outputs": [],
"source": [
"# Standard SQD helper functions (From SQD Tutorial)\n",
"\n",
"from typing import Sequence\n",
"import rustworkx\n",
"from qiskit.providers import BackendV2\n",
"from qiskit import QuantumCircuit, QuantumRegister\n",
"\n",
"from rustworkx import NoEdgeBetweenNodes, PyGraph\n",
"\n",
"IBM_TWO_Q_GATES = {\"cx\", \"ecr\", \"cz\"}\n",
"\n",
"\n",
"def create_linear_chains(num_orbitals: int) -> PyGraph:\n",
" \"\"\"In zig-zag layout, there are two linear chains (with connecting\n",
" qubits between the chains). This function creates those two linear\n",
" chains: a rustworkx PyGraph with two disconnected linear chains.\n",
" Each chain contains `num_orbitals` number of nodes, that is, in the\n",
" final graph there are `2 * num_orbitals` number of nodes.\n",
"\n",
" Args:\n",
" num_orbitals (int): Number orbitals or nodes in each linear chain.\n",
" They are also known as alpha-alpha interaction qubits.\n",
"\n",
" Returns:\n",
" A rustworkx.PyGraph with two disconnected linear chains each with\n",
" `num_orbitals` number of nodes.\n",
" \"\"\"\n",
" G = rustworkx.PyGraph()\n",
"\n",
" for n in range(num_orbitals):\n",
" G.add_node(n)\n",
"\n",
" for n in range(num_orbitals - 1):\n",
" G.add_edge(n, n + 1, None)\n",
"\n",
" for n in range(num_orbitals, 2 * num_orbitals):\n",
" G.add_node(n)\n",
"\n",
" for n in range(num_orbitals, 2 * num_orbitals - 1):\n",
" G.add_edge(n, n + 1, None)\n",
"\n",
" return G\n",
"\n",
"\n",
"def create_lucj_zigzag_layout(\n",
" num_orbitals: int, backend_coupling_graph: PyGraph\n",
") -> tuple[PyGraph, int]:\n",
" \"\"\"This function creates the complete zigzag graph that 'can be mapped'\n",
" to an IBM QPU with heavy-hex connectivity (the zigzag must be an\n",
" isomorphic sub-graph to the QPU/backend coupling graph for it to be\n",
" mapped). The zigzag pattern includes both linear chains (alpha-alpha\n",
" interactions) and connecting qubits between the linear chains\n",
" (alpha-beta interactions).\n",
"\n",
" Args:\n",
" num_orbitals (int): Number of orbitals, that is, number of nodes in\n",
" each alpha-alpha linear chain.\n",
" backend_coupling_graph (PyGraph): The coupling graph of the backend\n",
" on which the LUCJ ansatz will be mapped and run. This function takes\n",
" the coupling graph as a undirected `rustworkx.PyGraph` where there\n",
" is only one 'undirected' edge between two nodes, that is, qubits.\n",
" Usually, the coupling graph of a IBM backend is directed (for\n",
" example, Eagle devices such as ibm_brisbane) or may have two edges\n",
" between two nodes (for example, Heron `ibm_torino`). A user\n",
" needs to make such graphs undirected or remove duplicate edges\n",
" (or do both) to make them compatible with this function.\n",
"\n",
" Returns:\n",
" G_new (PyGraph): The graph with IBM backend compliant zigzag pattern.\n",
" num_alpha_beta_qubits (int): Number of connecting qubits between the\n",
" linear chains in the zigzag pattern. While we want as many\n",
" connecting (alpha-beta) qubits between the linear (alpha-alpha)\n",
" chains, we cannot accommodate all due to qubit and connectivity\n",
" constraints of backends. This is the maximum number of connecting\n",
" qubits the zigzag pattern can have while being backend compliant\n",
" (that is, isomorphic to backend coupling graph).\n",
" \"\"\"\n",
" isomorphic = False\n",
" G = create_linear_chains(num_orbitals=num_orbitals)\n",
"\n",
" num_iters = num_orbitals\n",
" while not isomorphic:\n",
" G_new = G.copy()\n",
" num_alpha_beta_qubits = 0\n",
" for n in range(num_iters):\n",
" if n % 4 == 0:\n",
" new_node = 2 * num_orbitals + num_alpha_beta_qubits\n",
" G_new.add_node(new_node)\n",
" G_new.add_edge(n, new_node, None)\n",
" G_new.add_edge(new_node, n + num_orbitals, None)\n",
" num_alpha_beta_qubits = num_alpha_beta_qubits + 1\n",
" isomorphic = rustworkx.is_subgraph_isomorphic(\n",
" backend_coupling_graph, G_new\n",
" )\n",
" num_iters -= 1\n",
"\n",
" return G_new, num_alpha_beta_qubits\n",
"\n",
"\n",
"def lightweight_layout_error_scoring(\n",
" backend: BackendV2,\n",
" virtual_edges: Sequence[Sequence[int]],\n",
" physical_layouts: Sequence[int],\n",
" two_q_gate_name: str,\n",
") -> list[list[list[int], float]]:\n",
" \"\"\"Lightweight and heuristic function to score isomorphic layouts. There\n",
" can be many zigzag patterns, each with different set of physical qubits,\n",
" that can be mapped to a backend. Some of them might include fewer noise\n",
" qubits and couplings than others. This function computes a simple error\n",
" score for each such layout. It sums up 2Q gate error for all couplings\n",
" in the zigzag pattern (layout) and measurement of errors of physical\n",
" qubits in the layout to compute the error score.\n",
"\n",
" Note:\n",
" This lightweight scoring can be refined using concepts such as\n",
" mapomatic.\n",
"\n",
" Args:\n",
" backend (BackendV2): A backend.\n",
" virtual_edges (Sequence[Sequence[int]]): Edges in the device-\n",
" compliant zigzag pattern where nodes are numbered from 0 to (2 *\n",
" num_orbitals + num_alpha_beta_qubits).\n",
" physical_layouts (Sequence[int]): All physical layouts of the zigzag\n",
" pattern that are isomorphic to each other and to the larger backend\n",
" coupling map.\n",
" two_q_gate_name (str): The name of the two-qubit gate of the\n",
" backend. The name is used for fetching two-qubit gate error from\n",
" backend properties.\n",
"\n",
" Returns:\n",
" scores (list): A list of lists where each sublist contains two\n",
" items. First item is the layout, and second item is a float\n",
" representing error score of the layout. The layouts in the `scores`\n",
" are sorted in the ascending order of error score.\n",
" \"\"\"\n",
" props = backend.properties()\n",
" scores = []\n",
" for layout in physical_layouts:\n",
" total_2q_error = 0\n",
" for edge in virtual_edges:\n",
" physical_edge = (layout[edge[0]], layout[edge[1]])\n",
" try:\n",
" ge = props.gate_error(two_q_gate_name, physical_edge)\n",
" except Exception:\n",
" ge = props.gate_error(two_q_gate_name, physical_edge[::-1])\n",
" total_2q_error += ge\n",
" total_measurement_error = 0\n",
" for qubit in layout:\n",
" meas_error = props.readout_error(qubit)\n",
" total_measurement_error += meas_error\n",
" scores.append([layout, total_2q_error + total_measurement_error])\n",
" return sorted(scores, key=lambda x: x[1])\n",
"\n",
"\n",
"def _make_backend_cmap_pygraph(backend: BackendV2) -> PyGraph:\n",
" graph = backend.coupling_map.graph\n",
" if not graph.is_symmetric():\n",
" graph.make_symmetric()\n",
" backend_coupling_graph = graph.to_undirected()\n",
"\n",
" edge_list = backend_coupling_graph.edge_list()\n",
" removed_edge = []\n",
" for edge in edge_list:\n",
" if set(edge) in removed_edge:\n",
" continue\n",
" try:\n",
" backend_coupling_graph.remove_edge(edge[0], edge[1])\n",
" removed_edge.append(set(edge))\n",
" except NoEdgeBetweenNodes:\n",
" pass\n",
"\n",
" return backend_coupling_graph\n",
"\n",
"\n",
"def get_zigzag_physical_layout(\n",
" num_orbitals: int, backend: BackendV2, score_layouts: bool = True\n",
") -> tuple[list[int], int]:\n",
" \"\"\"The main function that generates the zigzag pattern\n",
" with physical qubits that can be used as an `intial_layout` in a\n",
" preset passmanager/transpiler.\n",
"\n",
" Args:\n",
" num_orbitals (int): Number of orbitals.\n",
" backend (BackendV2): A backend.\n",
" score_layouts (bool): Optional. If `True`, it uses the\n",
" `lightweight_layout_error_scoring` function to score the\n",
" isomorphic layouts and returns the layout with\n",
" fewer erroneous qubits.\n",
" If `False`, returns the first isomorphic subgraph.\n",
"\n",
" Returns:\n",
" A tuple of device compliant layout (list[int]) with zigzag pattern\n",
" and an int representing number of alpha-beta-interactions.\n",
" \"\"\"\n",
" backend_coupling_graph = _make_backend_cmap_pygraph(backend=backend)\n",
"\n",
" G, num_alpha_beta_qubits = create_lucj_zigzag_layout(\n",
" num_orbitals=num_orbitals,\n",
" backend_coupling_graph=backend_coupling_graph,\n",
" )\n",
"\n",
" isomorphic_mappings = rustworkx.vf2_mapping(\n",
" backend_coupling_graph, G, subgraph=True\n",
" )\n",
" isomorphic_mappings = list(isomorphic_mappings)\n",
"\n",
" edges = list(G.edge_list())\n",
"\n",
" layouts = []\n",
" for mapping in isomorphic_mappings:\n",
" initial_layout = [None] * (2 * num_orbitals + num_alpha_beta_qubits)\n",
" for key, value in mapping.items():\n",
" initial_layout[value] = key\n",
" layouts.append(initial_layout)\n",
"\n",
" two_q_gate_name = IBM_TWO_Q_GATES.intersection(\n",
" backend.configuration().basis_gates\n",
" ).pop()\n",
"\n",
" if score_layouts:\n",
" scores = lightweight_layout_error_scoring(\n",
" backend=backend,\n",
" virtual_edges=edges,\n",
" physical_layouts=layouts,\n",
" two_q_gate_name=two_q_gate_name,\n",
" )\n",
"\n",
" return scores[0][0][:-num_alpha_beta_qubits], num_alpha_beta_qubits\n",
"\n",
" return layouts[0][:-num_alpha_beta_qubits], num_alpha_beta_qubits"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "87f8e3ec",
"metadata": {},
"outputs": [],
"source": [
"from qiskit.transpiler import generate_preset_pass_manager\n",
"import ffsim\n",
"\n",
"# Initial LUCJ ansatz layout\n",
"initial_layout, _ = get_zigzag_physical_layout(norb, backend=backend)\n",
"\n",
"# Initialize a pass manager\n",
"pass_manager = generate_preset_pass_manager(\n",
" optimization_level=3, backend=backend, initial_layout=initial_layout\n",
")\n",
"\n",
"pass_manager.pre_init = ffsim.qiskit.PRE_INIT"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "b4d480b3",
"metadata": {},
"source": [
"\n",
"\n",
"## Pasos 3 y 4: Ejecutar con Qiskit y realizar el posprocesamiento con Qiskit Serverless\n",
"\n",
"En este caso, combinamos el paso 3 (Ejecución) y el paso 4 (Posprocesamiento), ya que el contexto de aplicación del modelo de disolvente implícito requiere un bucle de retroalimentación iterativo que ejecuta varios ciclos de ejecución y posprocesamiento para mejorar el cálculo final.\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "8715bb6b",
"metadata": {},
"source": [
"\n",
"\n",
"### 3.1 Calcular la energía de Hartree-Fock restringida\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3aa879ad",
"metadata": {},
"outputs": [],
"source": [
"# Run the kernel to get the RHF energy\n",
"mf_opt = scf.RHF(mol_opt).PCM(cm)\n",
"hf_e = float(mf_opt.kernel())"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "480dd476",
"metadata": {},
"outputs": [],
"source": [
"print(f\"Restricted Hartree-Fock Energy: {hf_e}\")"
]
},
{
"cell_type": "markdown",
"id": "fd0145c6",
"metadata": {},
"source": [
"\n",
"\n",
"### 3.2: Establecer la energía de referencia clásica con CASCI\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f629119d",
"metadata": {},
"outputs": [],
"source": [
"# Setup a Serverless Client\n",
"worker = client.load(\"classical_simulation\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bb9d2407",
"metadata": {},
"outputs": [],
"source": [
"from json.encoder import JSONEncoder\n",
"\n",
"ao_labels = [\"C 2s\", \"C 2p\", \"N 2s\", \"N 2p\", \"H 1s\"]\n",
"data_e = JSONEncoder().encode([mol_opt.tostring(), eps_water, ao_labels])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "93874c58",
"metadata": {},
"outputs": [],
"source": [
"serverless_job = worker.run(data=data_e)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "756f0fdf",
"metadata": {},
"outputs": [],
"source": [
"# Optionally, check the Serverless status feedback\n",
"# Don't sit here and stare at the feedback unless debugging.\n",
"# You can go develop something else while the Serverless job runs.\n",
"_ = feedback_serverless(serverless_job)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d3ede43c",
"metadata": {},
"outputs": [],
"source": [
"# If you make a mistake and need to cancel something\n",
"\n",
"# for job in client.jobs():\n",
"# job.cancel()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7379c49f",
"metadata": {},
"outputs": [],
"source": [
"from json.decoder import JSONDecoder\n",
"\n",
"CASCI_E = JSONDecoder().decode(serverless_job.result()[\"outputs\"])[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0a5f4b88",
"metadata": {},
"outputs": [],
"source": [
"# We have approximated the red, classical baseline from\n",
"# Figure 1 for Methanol (North-West panel)\n",
"print(f\"CASCI/IEF-PCM(cc-pVDZ): E={CASCI_E}\")"
]
},
{
"cell_type": "markdown",
"id": "b22a1b00",
"metadata": {},
"source": [
"\n",
"\n",
"## Configurar los parámetros de la aplicación\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "618375af",
"metadata": {},
"outputs": [],
"source": [
"# Systematically vary these parameters to improve hardware results\n",
"\n",
"# Set to \"True\" to run on real hardware\n",
"use_hardware = True\n",
"\n",
"# Error suppression/mitigation options\n",
"# >> Configure within Sampler primitive\n",
"\n",
"# Transpiler Options\n",
"optimization_level = 3\n",
"\n",
"# Heartwood algorithm options\n",
"n_iter = 15 # How many update loops to run\n",
"resample = 1 # (resample=1 -> resample the QPU after every\n",
"# update loop; resample=n_iter -> sample QPU only once)\n",
"shots = 10000\n",
"\n",
"# SQD options\n",
"energy_tol = 1e-4\n",
"occupancies_tol = 1e-3\n",
"max_iterations = 12\n",
"\n",
"# Eigenstate solver options\n",
"num_batches = 5\n",
"samples_per_batch = 300\n",
"symmetrize_spin = True\n",
"carryover_threshold = 1e-5\n",
"max_cycle = 200\n",
"\n",
"# Classical post-processing options\n",
"local = (\n",
" False # Remote, Serverless (False) versus Local Post-Processing (True)\n",
")\n",
"mem = 16 # Memory allocated to each diagonalization worker (Gb)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ce75a126",
"metadata": {},
"outputs": [],
"source": [
"# Heartwood algorithm subroutines\n",
"import os\n",
"import numpy as np\n",
"import pyscf\n",
"from pyscf import ao2mo, cc\n",
"from functools import reduce\n",
"import ffsim\n",
"\n",
"from json.encoder import JSONEncoder\n",
"from json.decoder import JSONDecoder\n",
"import time\n",
"\n",
"from functools import partial\n",
"from qiskit_addon_sqd.fermion import (\n",
" SCIResult,\n",
" diagonalize_fermionic_hamiltonian,\n",
" solve_sci_batch,\n",
")\n",
"\n",
"\n",
"def update_rdm(casci_object, dmas):\n",
" \"\"\"\n",
" Inputs:\n",
" mc -> CASCI object\n",
" dmas -> Spin-summed 1-particle reduced density matrix\n",
"\n",
" This function returns the CASCI/SQD one-body density matrix in\n",
" the full basis of atomic orbitals, written as the sum (last line)\n",
" of two terms:\n",
" - a contribution from the core orbitals,\n",
" np.dot(mocore, mocore.conj().T) * 2, (core = inactive and doubly-occupied)\n",
" - a contribution from the active-space orbitals and electrons (dmas)\n",
" rotated from the active-space to the AO basis (the reduce operation)\n",
"\n",
" Outputs:\n",
" rho_approximation: The CASCI/SQD one-body density matrix\n",
" in the full basis of atomic orbitals\n",
"\n",
" \"\"\"\n",
" mo_coeff = casci_object.mo_coeff\n",
" ncore = casci_object.ncore\n",
" ncas = casci_object.ncas\n",
" mocore = mo_coeff[:, :ncore]\n",
" mocas = mo_coeff[:, ncore : ncore + ncas]\n",
" dm1 = np.dot(mocore, mocore.conj().T) * 2\n",
"\n",
" rho_approximation = dm1 + reduce(np.dot, (mocas, dmas, mocas.conj().T))\n",
" return rho_approximation\n",
"\n",
"\n",
"def run_active_space_calculation(\n",
" h1e_cas, h2e_cas, norb, ne_act, orbs, fermilevel, ecore\n",
"):\n",
" # ----- perform an HF and a CCSD calculation in the active space\n",
" from pyscf import tools\n",
" from datetime import datetime\n",
"\n",
" now = datetime.now().strftime(\"%H:%M:%S\")\n",
" print(\">>>>> ACTIVE SPACE CALCULATIONS \")\n",
" tools.fcidump.from_integrals(\n",
" f\"as_fcidump_{now}.txt\",\n",
" h1e_cas,\n",
" h2e_cas,\n",
" norb,\n",
" ne_act,\n",
" ms=0,\n",
" nuc=ecore,\n",
" ) # Forcefully represents the active space in the correct structure\n",
" mf_as = tools.fcidump.to_scf(f\"as_fcidump_{now}.txt\")\n",
" os.remove(f\"as_fcidump_{now}.txt\")\n",
" mf_as.kernel()\n",
" print(\">>>>> RUNNING CCSD\")\n",
"\n",
" mf_cc = cc.CCSD(mf_as)\n",
" mf_cc.kernel()\n",
" orbts = mf_as.mo_coeff\n",
" t1, t2 = mf_cc.t1, mf_cc.t2\n",
" print(\">>>>> UPDATED t1, t2 PARAMETERS\")\n",
"\n",
" # ----- update the HF orbitals\n",
" active = list(\n",
" range(fermilevel - ne_act // 2, fermilevel - ne_act // 2 + norb)\n",
" )\n",
" orbs[:, active] = np.dot(orbs[:, active], orbts)\n",
" return orbs, t1, t2\n",
"\n",
"\n",
"def get_lucj(norb, num_elec_a, num_elec_b, t1, t2, n_reps=1):\n",
" print(\">>>>> CONSTRUCTING LUCJ CIRCUIT\")\n",
"\n",
" alpha_alpha_indices = [(p, p + 1) for p in range(norb - 1)]\n",
" alpha_beta_indices = [(p, p) for p in range(0, norb, 4)]\n",
"\n",
" ucj_op = ffsim.UCJOpSpinBalanced.from_t_amplitudes(\n",
" t1=t1, # <---- Update t1 each loop\n",
" t2=t2, # <---- Update t2 each loop\n",
" n_reps=n_reps,\n",
" interaction_pairs=(alpha_alpha_indices, alpha_beta_indices),\n",
" )\n",
" nelec = (num_elec_a, num_elec_b)\n",
"\n",
" # create an empty quantum circuit\n",
" qubits = QuantumRegister(2 * norb, name=\"q\")\n",
" circuit = QuantumCircuit(qubits)\n",
"\n",
" # prepare Hartree-Fock state as the reference state\n",
" # and append it to the quantum circuit\n",
" circuit.append(ffsim.qiskit.PrepareHartreeFockJW(norb, nelec), qubits)\n",
"\n",
" # apply the UCJ operator to the reference state\n",
" circuit.append(ffsim.qiskit.UCJOpSpinBalancedJW(ucj_op), qubits)\n",
" circuit.measure_all()\n",
"\n",
" return circuit\n",
"\n",
"\n",
"# Classical diagonalization engine sent to HPC\n",
"def classically_diagonalize(\n",
" bit_array=None, # Bit string array (only needed if locally processing data)\n",
" nuclear_repulsion_energy=None, # Electronic energy from the core orbitals\n",
" hcore=None, # 1-electron hamiltonian integrals\n",
" eri=None, # 2-electron hamiltonian integrals\n",
" num_orbitals=None, # Number of spatial orbitals\n",
" nelec=None, # Number of electrons\n",
" num_elec_a=None, # Alpha orbitals\n",
" num_elec_b=None, # Beta orbitals\n",
" job_id=None, # QPU bitstring Job ID\n",
" client=None, # Diagonalization engine worker\n",
" energy_tol=1e-4, # SQD option\n",
" occupancies_tol=1e-3, # SQD option\n",
" max_iterations=12, # SQD option\n",
" num_batches=8, # Eigenstate solver option\n",
" samples_per_batch=300, # Eigenstate solver option\n",
" symmetrize_spin=False, # Eigenstate solver option\n",
" carryover_threshold=1e-5, # Eigenstate solver option\n",
" max_cycle=200, # Eigenstate solver option\n",
" local=True, # Remote vs Local Diagonalization\n",
" mem=16.0, # Memory per Serverless Worker (Gb)\n",
"):\n",
" print(\">>>>> STARTING DIAGONALIZATION ENGINE \")\n",
" # Pass options to the built-in eigensolver. If you just want to use\n",
" # the defaults, you can omit this step, in which case you would not\n",
" # specify the sci_solver argument in the call to\n",
" # diagonalize_fermionic_hamiltonian below.\n",
" if local:\n",
" sci_solver = partial(\n",
" solve_sci_batch, spin_sq=0.0, max_cycle=max_cycle\n",
" )\n",
"\n",
" # List to capture intermediate results\n",
" result_history = []\n",
"\n",
" def callback(results: list[SCIResult]):\n",
" result_history.append(results)\n",
" iteration = len(result_history)\n",
" print(f\">>>>> SQD ITERATION {iteration}\")\n",
" for i, result in enumerate(results):\n",
" print(f\">>>>> SUBSAMPLE {i}\")\n",
" print(\n",
" f\">>>>> \\tENERGY: {result.energy + nuclear_repulsion_energy}\"\n",
" )\n",
" print(\n",
" f\">>>>> \\tSUBSPACE DIMENSION: {np.prod(result.sci_state.amplitudes.shape)}\"\n",
" )\n",
"\n",
" result = diagonalize_fermionic_hamiltonian(\n",
" hcore,\n",
" eri,\n",
" bit_array,\n",
" samples_per_batch=samples_per_batch,\n",
" norb=num_orbitals,\n",
" nelec=(nelec // 2, nelec // 2),\n",
" num_batches=num_batches,\n",
" energy_tol=energy_tol,\n",
" occupancies_tol=occupancies_tol,\n",
" max_iterations=max_iterations,\n",
" sci_solver=sci_solver,\n",
" symmetrize_spin=symmetrize_spin,\n",
" carryover_threshold=carryover_threshold,\n",
" callback=callback,\n",
" seed=12345,\n",
" )\n",
"\n",
" result = (result.energy, result.rdm1, result.rdm2)\n",
"\n",
" else:\n",
" # Serverless Logic\n",
" print(\n",
" f\">>>>> SENDING QISKIT RUNTIME JOB {job_id} TO QISKIT SERVERLESS\"\n",
" )\n",
"\n",
" data = [\n",
" job_id,\n",
" hcore.tolist(),\n",
" eri.tolist(),\n",
" int(num_orbitals),\n",
" float(nuclear_repulsion_energy),\n",
" int(num_elec_a),\n",
" int(num_elec_b),\n",
" ]\n",
"\n",
" # Encode the execution dependencies with the JSONEncoder\n",
" data_e = JSONEncoder().encode(data)\n",
"\n",
" # Send to Serverless\n",
" worker = client.load(\"diagonalization_engine\")\n",
" serverless_job = worker.run(\n",
" data=data_e,\n",
" energy_tol=energy_tol, # SQD option\n",
" occupancies_tol=occupancies_tol, # SQD option\n",
" max_iterations=max_iterations, # SQD option\n",
" symmetrize_spin=symmetrize_spin, # Eigenstate solver option\n",
" carryover_threshold=carryover_threshold, # Eigenstate solver option\n",
" num_batches=num_batches, # Eigenstate solver option\n",
" samples_per_batch=samples_per_batch, # Eigenstate solver option\n",
" max_cycle=max_cycle, # Eigenstate solver option\n",
" mem=mem, # Memory per Worker (Gb)\n",
" )\n",
"\n",
" # Wait for the job to execute\n",
" _ = feedback_serverless(serverless_job)\n",
"\n",
" o_data = JSONDecoder().decode(serverless_job.result()[\"outputs\"])\n",
" result = (o_data[1], np.array(o_data[2]), np.array(o_data[3]))\n",
"\n",
" print(f\">>>>>>>>>> Active Space Energy: {o_data[1]}\")\n",
" print(f\">>>>>>>>>> rdm1: {o_data[2]}\")\n",
" print(f\">>>>>>>>>> rdm2: {o_data[3]}\")\n",
"\n",
" return result"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d1e23956",
"metadata": {},
"outputs": [],
"source": [
"# The Heartwood algorithm\n",
"import numpy as np\n",
"\n",
"from qiskit_ibm_runtime import SamplerV2 as Sampler\n",
"from qiskit_addon_sqd.counts import generate_bit_array_uniform\n",
"\n",
"mc = pyscf.mcscf.CASCI(mf_opt, ncas=norb, nelecas=ne_act).PCM(cm)\n",
"mc.with_solvent.method = mf_opt.with_solvent.method # Here we make sure\n",
"# that mc is also using the same solvent method defined earlier (IEF-PCM)\n",
"mc.with_solvent.eps = mf_opt.with_solvent.eps # Set the dielectric parameters\n",
"mc.mo_coeff = mo_avas.copy() # Update the molecular orbitals to include\n",
"# those computed in the presence of the solvent\n",
"\n",
"h1e_cas, ecore = (\n",
" mc.get_h1eff()\n",
") # <-- h1eff is the 1-electron hamiltonian integrals. h1e_cas is\n",
"# a common alias. ecore is the electronic energy from the core orbitals.\n",
"h2e_cas = ao2mo.restore(\n",
" 1, mc.get_h2eff(), norb\n",
") # <-- get the 2-electron hamiltonian integrals\n",
"\n",
"mc.mo_coeff, t1, t2 = run_active_space_calculation(\n",
" h1e_cas,\n",
" h2e_cas,\n",
" norb,\n",
" ne_act,\n",
" mo_avas.copy(),\n",
" mf_opt.mol.nelectron // 2,\n",
" ecore,\n",
")\n",
"\n",
"# Sampler primitive options\n",
"sampler = Sampler(mode=backend)\n",
"\n",
"# Explore error suppression techniques and see if they can improve result quality\n",
"sampler.options.dynamical_decoupling.enable = True\n",
"sampler.options.dynamical_decoupling.sequence_type = \"XY4\"\n",
"sampler.options.twirling.enable_measure = True\n",
"sampler.options.environment.job_tags = [\"TUT_ISC\"]\n",
"# sampler.options.twirling.enable_gates = False\n",
"# sampler.options.twirling.num_randomizations = 10\n",
"# sampler.options.twirling.shots_per_randomization = 1024\n",
"\n",
"# initial approximation for rdm1\n",
"with_solvent_e, with_solvent_v = None, None # Don't touch\n",
"data = []\n",
"for iiter in range(n_iter):\n",
" print(f\">>>>> IMPLICIT SOLVENT ITERATION {iiter+1}/{n_iter}\")\n",
" if with_solvent_v is not None:\n",
" # Subsequent update loops enter here\n",
" mc.get_hcore = lambda *args: mc._scf.get_hcore() + with_solvent_v\n",
" else:\n",
" # First update loop starts here\n",
" # hcore is the CAS space (classically computed) 1-electron\n",
" # hamiltonian, which we default to at the start of the routine.\n",
" mc.get_hcore = (\n",
" lambda *args: mc._scf.get_hcore()\n",
" ) # REF: https://pyscf.org/pyscf_api_docs/pyscf.mcscf.html#pyscf.scf.hf.CASBase.get_h1cas\n",
"\n",
" # Alias mapping\n",
" # hcore : h1e_cas : h1e_eff\n",
" # nuclear_repulsion_energy : ecore\n",
" # eri : h2e_cas : h2e_eff\n",
"\n",
" h1e_cas, ecore = (\n",
" mc.get_h1eff()\n",
" ) # <-- h1eff is the 1-electron hamiltonian integrals. h1e_cas is a\n",
" # common alias. ecore is the electronic energy from the core orbitals.\n",
" h2e_cas = ao2mo.restore(\n",
" 1, mc.get_h2eff(), norb\n",
" ) # <-- get the 2-electron hamiltonian integrals\n",
"\n",
" mc.mo_coeff, t1, t2 = run_active_space_calculation(\n",
" h1e_cas,\n",
" h2e_cas,\n",
" norb,\n",
" ne_act,\n",
" mo_avas.copy(),\n",
" mf_opt.mol.nelectron // 2,\n",
" ecore,\n",
" )\n",
"\n",
" if use_hardware:\n",
" if (\n",
" iiter % resample == 0\n",
" ): # <-- Toggle how often you refresh your bitstrings here. The\n",
" # developer suggests that you do it every time, but benevolently\n",
" # provides the freedom to disagree with him via the resample\n",
" # control variable.\n",
" # The \"Quantum-Centric\" part\n",
" print(\">>>>> GENERATING BITSTRINGS USING QUANTUM HARDWARE\")\n",
" # LUCJ Ansatz construction\n",
" circuit = get_lucj(norb, num_elec_a, num_elec_b, t1, t2, n_reps=1)\n",
"\n",
" print(f\">>>>> TRANSPILING LUCJ TO {backend.name}\")\n",
" isa_circuit = pass_manager.run(circuit)\n",
" print(f\">>>>> SUBMITTING ISA_CIRCUIT TO {backend.name}\")\n",
" job = sampler.run(\n",
" [isa_circuit], shots=shots\n",
" ) # <----- Error Suppression/Mitigation configured performed upstream\n",
" job_id = str(job.job_id())\n",
"\n",
" timer = 0\n",
" while job.status() != \"DONE\":\n",
" timer += 10\n",
" print(\n",
" f\">>>>> [{timer}s] RUNTIME JOB {job_id}: {job.status()}\"\n",
" )\n",
" time.sleep(10)\n",
"\n",
" primitive_result = job.result()\n",
" print(f\">>>>> RETRIEVED {job_id} FROM {backend.name}\")\n",
"\n",
" pub_result = primitive_result[0]\n",
" bit_array = pub_result.data.meas\n",
"\n",
" else:\n",
" print(\">>>>> GENERATING BITSTRINGS CLASSICALLY\")\n",
" rng = np.random.default_rng(24)\n",
" bit_array = generate_bit_array_uniform(\n",
" 100_000, 2 * norb, rand_seed=rng\n",
" ) # <-- Sample bitstrings from a uniform distribution. This is\n",
" # useful for debug, but runs out of steam on large systems\n",
" job_id = float(\n",
" \"nan\"\n",
" ) # <-- we will check that valid job_id's were passed during grading\n",
" local = True\n",
"\n",
" # The \"Classical Post-processing\" part\n",
" result = classically_diagonalize(\n",
" bit_array=bit_array,\n",
" nuclear_repulsion_energy=ecore, # Electronic energy from the core orbitals\n",
" hcore=h1e_cas, # 1-electron hamiltonian integrals\n",
" eri=h2e_cas, # 2-electron hamiltonian integrals\n",
" num_orbitals=norb, # Number of spatial orbitals\n",
" nelec=ne_act, # Number of electrons\n",
" num_elec_a=ne_act // 2, # Alpha orbitals\n",
" num_elec_b=ne_act // 2, # Beta orbitals\n",
" job_id=job_id, # QPU bitstring Job ID\n",
" client=client, # Diagonalization engine worker\n",
" energy_tol=energy_tol, # SQD option\n",
" occupancies_tol=occupancies_tol, # SQD option\n",
" max_iterations=max_iterations, # SQD option\n",
" num_batches=num_batches, # Eigenstate solver option\n",
" samples_per_batch=samples_per_batch, # Eigenstate solver option\n",
" symmetrize_spin=symmetrize_spin, # Eigenstate solver option\n",
" carryover_threshold=carryover_threshold, # Eigenstate solver option\n",
" max_cycle=max_cycle, # Eigenstate solver option\n",
" local=local, # Remote vs Local Diagonalization\n",
" mem=mem, # Memory per Worker (Gb)\n",
" )\n",
"\n",
" # e : SQD-based estimate of the energy\n",
" # rdm1: Spin-summed 1-particle reduced density matrix\n",
" e, rdm1 = result[0], result[1]\n",
" rho_approximation = update_rdm(\n",
" mc, rdm1\n",
" ).copy() # <--- Reconstruct the one-body density matrix in the\n",
" # atomic orbital basis to update the external potential due to\n",
" # the solvent\n",
"\n",
" if with_solvent_e is not None:\n",
" # Subsequent update loops enter here\n",
" edup = np.einsum(\n",
" \"ij,ji->\", with_solvent_v, rho_approximation\n",
" ) # <-- edup: Incrementing the energy calculation with\n",
" # subsequent iterations\n",
" e += ecore + with_solvent_e - edup\n",
"\n",
" else:\n",
" # First update loop enters here\n",
" e += (\n",
" ecore # Pulled from the CAS space object (molecule's core energy)\n",
" )\n",
"\n",
" # Outputs:\n",
" # with_solvent_e : scalar energy correction due to solvent polarization\n",
" # with_solvent_v : Fock-like matrix to be added to the core Hamiltonian in SCF\n",
" with_solvent_e, with_solvent_v = mc.with_solvent._get_vind(\n",
" rho_approximation\n",
" )\n",
" data.append((iiter, float(e), job_id))\n",
" print(f\">>>>> END IITER {iiter}\")\n",
" print(f\">>>>> TOTAL ENERGY: {e}\\n\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3905922f",
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"from matplotlib.ticker import ScalarFormatter\n",
"\n",
"\n",
"def plot_data(data, baseline=0, name=None, save=False):\n",
" x_vals, y_vals, job_ids = zip(*data)\n",
" fig, ax = plt.subplots(figsize=(10, 6))\n",
"\n",
" # Plot line + markers\n",
" ax.plot(\n",
" x_vals,\n",
" y_vals,\n",
" color=\"navy\",\n",
" linewidth=2,\n",
" marker=\"o\",\n",
" markersize=5,\n",
" label=\"Energy trajectory\",\n",
" )\n",
" ax.axhline(\n",
" baseline,\n",
" color=\"red\",\n",
" linestyle=\"--\",\n",
" linewidth=1.5,\n",
" label=\"Reference energy\",\n",
" )\n",
"\n",
" # Force plain formatting\n",
" ax.yaxis.set_major_formatter(ScalarFormatter(useMathText=True))\n",
" ax.ticklabel_format(style=\"plain\", axis=\"y\")\n",
"\n",
" # Annotate each point with its exact value\n",
" for x, y, job_id in zip(x_vals, y_vals, job_ids):\n",
" ax.annotate(\n",
" f\"{y:.8f}, ID: {job_id}\",\n",
" (x, y),\n",
" textcoords=\"offset points\",\n",
" xytext=(0, 8), # vertical offset\n",
" ha=\"center\",\n",
" fontsize=8,\n",
" rotation=25,\n",
" color=\"navy\",\n",
" )\n",
"\n",
" # Annotate the Classical Reference line\n",
" for x, y in zip([0.5], [baseline]):\n",
" ax.annotate(\n",
" f\"{y:.5f}\",\n",
" (x, y),\n",
" textcoords=\"offset points\",\n",
" xytext=(0, 8), # vertical offset\n",
" ha=\"center\",\n",
" fontsize=8,\n",
" rotation=25,\n",
" color=\"red\",\n",
" )\n",
"\n",
" # Titles, labels, etc\n",
" ax.set_title(\n",
" f\"SQD/IEF-PCM(cc-pVDZ) - {name}\\nEnergy Convergence\",\n",
" fontsize=14,\n",
" fontweight=\"bold\",\n",
" pad=15,\n",
" )\n",
" ax.set_xlabel(\"Update Iterations\", fontsize=12)\n",
" ax.set_ylabel(\"Total Energy (Hartrees)\", fontsize=12)\n",
" ax.grid(True, linestyle=\"--\", linewidth=0.6, alpha=0.7)\n",
" ax.legend(frameon=True, loc=\"best\")\n",
" plt.tight_layout()\n",
"\n",
" if save:\n",
" plt.savefig(f\"./results/{name}_energy_convergence.png\")\n",
" return fig, ax"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6fdb639b",
"metadata": {},
"outputs": [],
"source": [
"# Plot your data\n",
"\n",
"fig, ax = plot_data(data, baseline=CASCI_E, name=molecule_name, save=True)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "75f48e6a-c7e4-46f3-9d39-a7a877427a04",
"metadata": {},
"source": [
"\n",
"\n",
"## Próximos pasos\n",
"\n",
"\n",
" Si te ha parecido interesante este trabajo, quizá te interesen los siguientes recursos:\n",
"\n",
" * [La Interfaz de Gestión de Recursos Cuánticos (QRMI)](https://github.com/qiskit-community/qrmi) : integra recursos cuánticos y clásicos en gestores de cargas de trabajo de HPC como Slurm, ampliando el modelo de computación distribuida mostrado en este tutorial\n",
" * [Guía de « Qiskit Serverless »](/docs/guides/serverless)\n",
" * [Qiskit Serverless GitHub](https://qiskit.github.io/qiskit-serverless/index.html)\n",
" * Tutorial [sobre la diagonalización cuántica basada en muestras de un hamiltoniano químico](/docs/tutorials/sample-based-quantum-diagonalization)\n",
" * [Descripción general de la diagonalización cuántica basada en muestras (SQD)](/docs/addons/qiskit-addon-sqd)\n",
" * [Implementar y ejecutar una plantilla para la simulación de estructuras electrónicas con un modelo de disolvente implícito](/docs/guides/function-template-chemistry-workflow) (plantilla de función SQD IEF-PCM de Qiskit, desarrollada conjuntamente por la Clínica Cleveland y IBM )\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"id": "a1b8767d",
"source": "© IBM Corp., 2017-2026"
}
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"display_name": "Python 3",
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