{
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"metadata": {},
"source": [
"---\n",
"title: \"Exemplos de estimadores\"\n",
"description: \"Exemplos práticos de uso da primitiva Estimator do IBM Quantum.\"\n",
"---\n",
"\n",
"\n",
"\n",
"# Exemplos de estimadores\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "10cf3d45-503c-41c9-b0a1-51c23e32bc5f",
"metadata": {
"tags": [
"version-info"
]
},
"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",
" O código desta página foi desenvolvido com base nos seguintes requisitos.\n",
" Recomendamos usar essas versões ou versões mais recentes.\n",
"\n",
" ```\n",
" qiskit[all]~=2.5.1\n",
" qiskit-ibm-runtime~=0.47.0\n",
" ```\n",
" \n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "3a98beb8-d1df-4daf-80ce-d51e4dc31cfa",
"metadata": {},
"source": [
"Os exemplos desta seção ilustram algumas formas comuns de usar o Estimator. Antes de executar estes exemplos, siga as instruções em [Instalar o Qiskit.](install-qiskit)\n",
"\n",
"\n",
" Todos esses exemplos utilizam as primitivas do tipo `IBM Quantum`, mas você também pode usar as primitivas básicas.\n",
"\n",
"\n",
"Calcule e interprete com eficiência os valores esperados dos operadores quânticos necessários para muitos algoritmos com o Estimator. Explore as aplicações em modelagem molecular, aprendizado de máquina e problemas complexos de otimização.\n",
"\n",
"\n",
"\n",
"## Executar um único experimento\n",
"\n",
"Use o Estimador para determinar o valor esperado de um único par circuito-observável.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "6bc4a6a3-612e-4ad8-9fe3-3d56a4cb9a8f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" > Expectation value: -0.008839779005524863\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",
"## Execute várias experiências em uma única tarefa\n",
"\n",
"Use o Estimator para determinar os valores esperados de vários pares de variáveis observáveis do 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: 0.9937888198757764\n",
">>> Standard errors for PUB 0: 1.7873718562576024\n",
">>> Expectation values for PUB 1: -0.1038961038961039\n",
">>> Standard errors for PUB 1: 1.3378580728628524\n",
">>> Expectation values for PUB 2: -0.6753246753246753\n",
">>> Standard errors for PUB 2: 1.7025095006727553\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",
"## Executar circuitos parametrizados\n",
"\n",
"Use o Estimator para executar três experimentos em uma única tarefa, aproveitando os valores dos parâmetros para aumentar a reutilização dos 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: [[ 9.89263974e-01 9.41062225e-01 7.87637085e-01 5.83548830e-01\n",
" 3.06029826e-01 -7.99943914e-03 -3.11567899e-01 -5.91343155e-01\n",
" -8.05892215e-01 -9.36139493e-01 -9.88853746e-01 -9.42703136e-01\n",
" -8.10609833e-01 -5.90522700e-01 -3.15465062e-01 -5.94830090e-03\n",
" 2.90851403e-01 5.85805082e-01 8.02200166e-01 9.41882681e-01\n",
" 9.90494657e-01]\n",
" [ 9.23012209e-03 3.12183240e-01 6.00163049e-01 8.02405280e-01\n",
" 9.48446323e-01 9.84956584e-01 9.42703136e-01 8.05071760e-01\n",
" 5.92984066e-01 3.07055395e-01 -3.07670736e-03 -3.18131541e-01\n",
" -5.60165854e-01 -7.94200727e-01 -9.43728705e-01 -9.91315112e-01\n",
" -9.39626428e-01 -8.03430849e-01 -5.79446553e-01 -3.04799143e-01\n",
" -4.92273178e-03]\n",
" [ 8.20455297e-03 -3.00696866e-01 -5.99752822e-01 -8.11635402e-01\n",
" -9.45574729e-01 -9.92340681e-01 -9.40036656e-01 -8.00354142e-01\n",
" -5.82113033e-01 -2.90441175e-01 1.84602442e-03 3.24284956e-01\n",
" 5.64883472e-01 8.04866646e-01 9.44138933e-01 9.93366250e-01\n",
" 9.48651437e-01 7.87842199e-01 5.58730057e-01 3.16490631e-01\n",
" -6.97387002e-03]\n",
" [ 9.91930454e-01 9.40446884e-01 7.89483109e-01 5.52986870e-01\n",
" 2.97209931e-01 2.25625207e-03 -3.11567899e-01 -5.92984066e-01\n",
" -8.08353581e-01 -9.48036095e-01 -9.90699771e-01 -9.42498022e-01\n",
" -8.09584264e-01 -5.96265887e-01 -3.07055395e-01 -2.05113824e-04\n",
" 3.02132663e-01 5.95240318e-01 7.93995613e-01 9.42703136e-01\n",
" 9.93776478e-01]]\n",
">>> Standard errors: [[0.00312841 0.00403472 0.00759426 0.00833516 0.01030326 0.01122794\n",
" 0.01128844 0.00813687 0.00865973 0.00513426 0.00345264 0.00388667\n",
" 0.00736782 0.01026783 0.01038179 0.01246262 0.01191826 0.01081814\n",
" 0.00850225 0.00496315 0.00269764]\n",
" [0.01123964 0.01177892 0.00819503 0.0073663 0.00427303 0.00289125\n",
" 0.00435623 0.0066848 0.01140235 0.00993107 0.00840459 0.00821741\n",
" 0.0097656 0.0082026 0.00506637 0.00357209 0.0041762 0.00767615\n",
" 0.00831383 0.01094546 0.01373803]\n",
" [0.0121451 0.01227001 0.00831633 0.00691503 0.00385729 0.00267653\n",
" 0.00454884 0.00722136 0.01080783 0.0098484 0.01141928 0.01035556\n",
" 0.00804047 0.0058858 0.00398198 0.00281015 0.00501157 0.00714316\n",
" 0.00954163 0.00878997 0.01213102]\n",
" [0.00290704 0.0047878 0.00638958 0.00768626 0.00875767 0.00887984\n",
" 0.01145493 0.00904896 0.00599187 0.00482709 0.00298048 0.00428419\n",
" 0.00690245 0.00855953 0.00997334 0.00862447 0.00870133 0.00872892\n",
" 0.00487633 0.00504538 0.00311229]]\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 IBM Quantum 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",
"## Use lotes e opções avançadas\n",
"\n",
"Explore o [modo de execução](/docs/guides/execution-modes) em lote e as opções avançadas para otimizar o desempenho dos circuitos nas QPUs.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "f009e993-98fe-451c-96f5-738153005543",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" > Expectation value: 0.025219391667376672\n",
" > Metadata: {'shots': 4096, 'target_precision': 0.015625, 'circuit_metadata': {}, 'resilience': {}, 'num_randomizations': 32}\n",
" > Another Expectation value: 0.002376355265112134\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",
"## Próximas etapas\n",
"\n",
"\n",
" * [Especifique opções avançadas de tempo de execução](runtime-options-overview).\n",
" * Pratique com primitivas seguindo a [lição](/learning/courses/variational-algorithm-design/cost-functions) sobre a função `Cost` em IBM Quantum® Learning.\n",
" * Saiba como fazer a transpilagem localmente na seção [Transpilagem](/docs/guides/transpile/).\n",
" * Consulte o guia [de comparação de configurações do transpiler](/docs/guides/circuit-transpilation-settings).\n",
" * Entenda os [limites da tarefa](/docs/guides/job-limits) ao enviar uma tarefa para uma QPU do IBM®.\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"id": "a1b8767d",
"source": "© IBM Corp., 2017-2026"
}
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