{
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"source": [
"---\n",
"title: \"Ejemplos de estimadores\"\n",
"description: \"Ejemplos prácticos del uso de la primitiva «Estimator» en Qiskit Runtime.\"\n",
"---\n",
"\n",
"\n",
"\n",
"# Ejemplos de estimadores\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "10cf3d45-503c-41c9-b0a1-51c23e32bc5f",
"metadata": {
"tags": [
"version-info"
]
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"source": [
"{/*\n",
" DO NOT EDIT THIS CELL!!!\n",
" This cell's content is generated automatically by a script. Anything you add\n",
" here will be removed next time the notebook is run. To add new content, create\n",
" a new cell before or after this one.\n",
" */}\n",
"\n",
"\n",
" \n",
" El código de esta página se ha desarrollado teniendo en cuenta los siguientes requisitos.\n",
" Recomendamos utilizar estas versiones o posteriores.\n",
"\n",
" ```\n",
" qiskit[all]~=2.4.0\n",
" qiskit-ibm-runtime~=0.46.1\n",
" ```\n",
" \n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "3a98beb8-d1df-4daf-80ce-d51e4dc31cfa",
"metadata": {},
"source": [
"Los ejemplos de esta sección muestran algunas formas habituales de utilizar Estimator. Antes de ejecutar estos ejemplos, sigue las instrucciones de [la sección «Instalar Qiskit».](install-qiskit)\n",
"\n",
"\n",
" Todos estos ejemplos utilizan las primitivas de Qiskit Runtime, pero también podrías utilizar las primitivas básicas.\n",
"\n",
"\n",
"Con Estimator, calcula e interpreta de forma eficiente los valores esperados de los operadores cuánticos necesarios para numerosos algoritmos. Descubre sus aplicaciones en el modelado molecular, el aprendizaje automático y los problemas de optimización complejos.\n",
"\n",
"\n",
"\n",
"## Realizar un único experimento\n",
"\n",
"Utiliza Estimator para calcular el valor esperado de un par circuito-observable.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "6bc4a6a3-612e-4ad8-9fe3-3d56a4cb9a8f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" > Expectation value: -0.0564042303172738\n",
" > Metadata: {'shots': 4096, 'target_precision': 0.015625, 'circuit_metadata': {}, 'resilience': {}, 'num_randomizations': 32}\n"
]
}
],
"source": [
"import numpy as np\n",
"from qiskit.circuit.library import iqp\n",
"from qiskit.transpiler import generate_preset_pass_manager\n",
"from qiskit.quantum_info import SparsePauliOp, random_hermitian\n",
"from qiskit_ibm_runtime import QiskitRuntimeService, EstimatorV2 as Estimator\n",
"\n",
"n_qubits = 50\n",
"\n",
"service = QiskitRuntimeService()\n",
"backend = service.least_busy(\n",
" operational=True, simulator=False, min_num_qubits=n_qubits\n",
")\n",
"\n",
"mat = np.real(random_hermitian(n_qubits, seed=1234))\n",
"circuit = iqp(mat)\n",
"observable = SparsePauliOp(\"Z\" * 50)\n",
"\n",
"pm = generate_preset_pass_manager(backend=backend, optimization_level=1)\n",
"isa_circuit = pm.run(circuit)\n",
"isa_observable = observable.apply_layout(isa_circuit.layout)\n",
"\n",
"estimator = Estimator(mode=backend)\n",
"job = estimator.run([(isa_circuit, isa_observable)])\n",
"result = job.result()\n",
"\n",
"print(f\" > Expectation value: {result[0].data.evs}\")\n",
"print(f\" > Metadata: {result[0].metadata}\")"
]
},
{
"cell_type": "markdown",
"id": "c8594feb-0516-4cca-9064-232906b980a4",
"metadata": {},
"source": [
"\n",
"\n",
"## Ejecutar varios experimentos en un solo trabajo\n",
"\n",
"Utilice Estimator para determinar los valores esperados de múltiples pares de variables observables del circuito.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "662e73e9-8454-470a-b6aa-e84343005ca6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
">>> Expectation values for PUB 0: 0.09218950064020487\n",
">>> Standard errors for PUB 0: 0.2666311918779662\n",
">>> Expectation values for PUB 1: -0.7159533073929961\n",
">>> Standard errors for PUB 1: 0.5443960702392404\n",
">>> Expectation values for PUB 2: -0.14271555996035679\n",
">>> Standard errors for PUB 2: 0.2714876601210801\n"
]
}
],
"source": [
"import numpy as np\n",
"from qiskit.circuit.library import iqp\n",
"from qiskit.transpiler import generate_preset_pass_manager\n",
"from qiskit.quantum_info import SparsePauliOp, random_hermitian\n",
"from qiskit_ibm_runtime import QiskitRuntimeService, EstimatorV2 as Estimator\n",
"\n",
"n_qubits = 50\n",
"\n",
"service = QiskitRuntimeService()\n",
"backend = service.least_busy(\n",
" operational=True, simulator=False, min_num_qubits=n_qubits\n",
")\n",
"\n",
"rng = np.random.default_rng()\n",
"mats = [np.real(random_hermitian(n_qubits, seed=rng)) for _ in range(3)]\n",
"\n",
"pubs = []\n",
"circuits = [iqp(mat) for mat in mats]\n",
"observables = [\n",
" SparsePauliOp(\"X\" * 50),\n",
" SparsePauliOp(\"Y\" * 50),\n",
" SparsePauliOp(\"Z\" * 50),\n",
"]\n",
"\n",
"# Get ISA circuits\n",
"pm = generate_preset_pass_manager(optimization_level=1, backend=backend)\n",
"\n",
"for qc, obs in zip(circuits, observables):\n",
" isa_circuit = pm.run(qc)\n",
" isa_obs = obs.apply_layout(isa_circuit.layout)\n",
" pubs.append((isa_circuit, isa_obs))\n",
"\n",
"estimator = Estimator(backend)\n",
"job = estimator.run(pubs)\n",
"job_result = job.result()\n",
"\n",
"for idx in range(len(pubs)):\n",
" pub_result = job_result[idx]\n",
" print(f\">>> Expectation values for PUB {idx}: {pub_result.data.evs}\")\n",
" print(f\">>> Standard errors for PUB {idx}: {pub_result.data.stds}\")"
]
},
{
"cell_type": "markdown",
"id": "5176c806-e00d-4def-93b4-64216bf8cc64",
"metadata": {},
"source": [
"\n",
"\n",
"## Ejecutar circuitos parametrizados\n",
"\n",
"Utiliza Estimator para ejecutar tres experimentos en un solo trabajo, aprovechando los valores de los parámetros para aumentar la reutilización de los circuitos.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "3f991c86-8bcd-4d9e-bed1-a6a7ac0cabb6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
">>> Expectation values: [[ 0.9821299 0.92848415 0.78219632 0.56555001 0.29732126 -0.02496591\n",
" -0.30928839 -0.5779298 -0.79292547 -0.92084995 -0.9806856 -0.93075378\n",
" -0.80014701 -0.57627916 -0.32496945 -0.00495192 0.29938456 0.56513735\n",
" 0.80117866 0.92580187 0.98151091]\n",
" [-0.00330128 0.30949472 0.58123108 0.78549759 0.9357057 0.97903496\n",
" 0.93240442 0.78879887 0.58267539 0.2948453 0.0041266 -0.29835291\n",
" -0.57339055 -0.78075201 -0.92477022 -0.97882863 -0.93075378 -0.79148116\n",
" -0.57958044 -0.30557445 0.00598356]\n",
" [-0.01031649 -0.34250749 -0.59257922 -0.80819387 -0.95159309 -0.99616033\n",
" -0.9336424 -0.78054568 -0.57112092 -0.30639977 0.00866585 0.30474913\n",
" 0.57627916 0.81149515 0.95035511 0.99224006 0.9530374 0.78673557\n",
" 0.57834246 0.30557445 -0.00866585]\n",
" [ 0.99616033 0.93446772 0.80344829 0.5841197 0.29401998 -0.01980766\n",
" -0.31300232 -0.59361087 -0.81170148 -0.94849814 -0.99327171 -0.93880064\n",
" -0.80860653 -0.58019943 -0.30186051 0.01856968 0.29009972 0.59835645\n",
" 0.80613057 0.94437155 0.98976411]]\n",
">>> Standard errors: [[0.00346988 0.00453617 0.00722056 0.00981693 0.01144016 0.01501324\n",
" 0.01334599 0.01100181 0.00916772 0.00689316 0.00381375 0.00555949\n",
" 0.00576968 0.01074419 0.01298665 0.01231428 0.0128399 0.00946472\n",
" 0.00819982 0.00494361 0.00359142]\n",
" [0.01087106 0.01070164 0.00869617 0.00735853 0.00475886 0.00351362\n",
" 0.00422178 0.00865889 0.00830071 0.01030088 0.01114086 0.01184411\n",
" 0.00958307 0.00740947 0.00577496 0.00417023 0.00434772 0.00825295\n",
" 0.00805684 0.01071724 0.01320466]\n",
" [0.01346985 0.01132597 0.01143045 0.00729025 0.00490636 0.00287136\n",
" 0.0051666 0.00718324 0.00899331 0.00980723 0.00957352 0.01211162\n",
" 0.00932736 0.00658862 0.00555066 0.00271584 0.00581507 0.00778402\n",
" 0.00935326 0.01223799 0.01214173]\n",
" [0.00297333 0.00520897 0.00730712 0.01099862 0.01320699 0.01250301\n",
" 0.0151248 0.00924768 0.00639241 0.00529221 0.00270411 0.00463968\n",
" 0.00729108 0.00685512 0.00993793 0.0101938 0.01109962 0.01130657\n",
" 0.00795711 0.00532976 0.00299901]]\n",
">>> Metadata: {'shots': 10016, 'target_precision': 0.01, 'circuit_metadata': {}, 'resilience': {}, 'num_randomizations': 32}\n"
]
}
],
"source": [
"import numpy as np\n",
"\n",
"from qiskit.circuit import QuantumCircuit, Parameter\n",
"from qiskit.quantum_info import SparsePauliOp\n",
"from qiskit.transpiler import generate_preset_pass_manager\n",
"from qiskit_ibm_runtime import QiskitRuntimeService, EstimatorV2 as Estimator\n",
"\n",
"service = QiskitRuntimeService()\n",
"backend = service.least_busy(operational=True, simulator=False)\n",
"\n",
"# Step 1: Map classical inputs to a quantum problem\n",
"theta = Parameter(\"θ\")\n",
"\n",
"chsh_circuit = QuantumCircuit(2)\n",
"chsh_circuit.h(0)\n",
"chsh_circuit.cx(0, 1)\n",
"chsh_circuit.ry(theta, 0)\n",
"\n",
"number_of_phases = 21\n",
"phases = np.linspace(0, 2 * np.pi, number_of_phases)\n",
"individual_phases = [[ph] for ph in phases]\n",
"\n",
"ZZ = SparsePauliOp.from_list([(\"ZZ\", 1)])\n",
"ZX = SparsePauliOp.from_list([(\"ZX\", 1)])\n",
"XZ = SparsePauliOp.from_list([(\"XZ\", 1)])\n",
"XX = SparsePauliOp.from_list([(\"XX\", 1)])\n",
"ops = [ZZ, ZX, XZ, XX]\n",
"\n",
"# Step 2: Optimize problem for quantum execution.\n",
"\n",
"pm = generate_preset_pass_manager(backend=backend, optimization_level=1)\n",
"chsh_isa_circuit = pm.run(chsh_circuit)\n",
"isa_observables = [\n",
" operator.apply_layout(chsh_isa_circuit.layout) for operator in ops\n",
"]\n",
"\n",
"# Step 3: Execute using Qiskit primitives.\n",
"\n",
"# Reshape observable array for broadcasting\n",
"reshaped_ops = np.fromiter(isa_observables, dtype=object)\n",
"reshaped_ops = reshaped_ops.reshape((4, 1))\n",
"\n",
"estimator = Estimator(backend, options={\"default_shots\": int(1e4)})\n",
"job = estimator.run([(chsh_isa_circuit, reshaped_ops, individual_phases)])\n",
"# Get results for the first (and only) PUB\n",
"pub_result = job.result()[0]\n",
"print(f\">>> Expectation values: {pub_result.data.evs}\")\n",
"print(f\">>> Standard errors: {pub_result.data.stds}\")\n",
"print(f\">>> Metadata: {pub_result.metadata}\")"
]
},
{
"cell_type": "markdown",
"id": "117454c0-c8df-48cd-8503-f4fd137d6991",
"metadata": {},
"source": [
"\n",
"\n",
"## Utiliza lotes y opciones avanzadas\n",
"\n",
"Explora el [modo de ejecución](/docs/guides/execution-modes) por lotes y las opciones avanzadas para optimizar el rendimiento de los circuitos en las QPU.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "f009e993-98fe-451c-96f5-738153005543",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" > Expectation value: -0.03391665163268988\n",
" > Metadata: {'shots': 4096, 'target_precision': 0.015625, 'circuit_metadata': {}, 'resilience': {}, 'num_randomizations': 32}\n",
" > Another Expectation value: -0.011113040458412918\n",
" > More Metadata: {'shots': 4096, 'target_precision': 0.015625, 'circuit_metadata': {}, 'resilience': {}, 'num_randomizations': 32}\n"
]
}
],
"source": [
"import numpy as np\n",
"from qiskit.circuit.library import iqp\n",
"from qiskit.transpiler import generate_preset_pass_manager\n",
"from qiskit.quantum_info import SparsePauliOp, random_hermitian\n",
"from qiskit_ibm_runtime import (\n",
" QiskitRuntimeService,\n",
" Batch,\n",
" EstimatorV2 as Estimator,\n",
")\n",
"\n",
"n_qubits = 15\n",
"\n",
"service = QiskitRuntimeService()\n",
"backend = service.least_busy(\n",
" operational=True, simulator=False, min_num_qubits=n_qubits\n",
")\n",
"\n",
"rng = np.random.default_rng(1234)\n",
"mat = np.real(random_hermitian(n_qubits, seed=rng))\n",
"circuit = iqp(mat)\n",
"mat = np.real(random_hermitian(n_qubits, seed=rng))\n",
"another_circuit = iqp(mat)\n",
"observable = SparsePauliOp(\"X\" * n_qubits)\n",
"another_observable = SparsePauliOp(\"Y\" * n_qubits)\n",
"\n",
"pm = generate_preset_pass_manager(optimization_level=1, backend=backend)\n",
"isa_circuit = pm.run(circuit)\n",
"another_isa_circuit = pm.run(another_circuit)\n",
"isa_observable = observable.apply_layout(isa_circuit.layout)\n",
"another_isa_observable = another_observable.apply_layout(\n",
" another_isa_circuit.layout\n",
")\n",
"\n",
"# The context manager automatically closes the batch.\n",
"with Batch(backend=backend) as batch:\n",
" estimator = Estimator(mode=batch)\n",
"\n",
" estimator.options.resilience_level = 1\n",
"\n",
" job = estimator.run([(isa_circuit, isa_observable)])\n",
" another_job = estimator.run(\n",
" [(another_isa_circuit, another_isa_observable)]\n",
" )\n",
" result = job.result()\n",
" another_result = another_job.result()\n",
"\n",
" # first job\n",
" print(f\" > Expectation value: {result[0].data.evs}\")\n",
" print(f\" > Metadata: {result[0].metadata}\")\n",
"\n",
" # second job\n",
" print(f\" > Another Expectation value: {another_result[0].data.evs}\")\n",
" print(f\" > More Metadata: {another_result[0].metadata}\")"
]
},
{
"cell_type": "markdown",
"id": "e207719d-da1b-4af2-ae63-4cb033865a1b",
"metadata": {},
"source": [
"\n",
"\n",
"## Próximos pasos\n",
"\n",
"\n",
" * [Especifica opciones avanzadas de tiempo de ejecución](runtime-options-overview).\n",
" * Practica con primitivas siguiendo la [lección](/learning/courses/variational-algorithm-design/cost-functions) sobre la función «Cost» en IBM Quantum® Learning.\n",
" * Descubre cómo realizar la transpilación de forma local en la sección «[Transpilación](/docs/guides/transpile/) ».\n",
" * Consulta la guía [sobre la configuración del transpilador Compare](/docs/guides/circuit-transpilation-settings).\n",
" * Lea «[Migrar a primitivas de `V2`](/docs/guides/v2-primitives) ».\n",
" * Ten en cuenta [los límites del trabajo](/docs/guides/job-limits) al enviarlo a una QPU de IBM®.\n",
"\n",
"\n"
]
},
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"cell_type": "markdown",
"metadata": {},
"id": "a1b8767d",
"source": "© IBM Corp., 2017-2026"
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