{ "cells": [ { "cell_type": "markdown", "id": "0be36083-7bf5-48b7-9241-7979d6853d0c", "metadata": {}, "source": [ "---\n", "title: \"Exemplos de amostras\"\n", "description: \"Exemplos práticos de uso da primitiva “Sampler” do IBM Quantum.\"\n", "---\n", "\n", "\n", "\n", "# Exemplos de amostras\n", "\n" ] }, { "cell_type": "markdown", "id": "ef0ce9e6-eee0-4b4b-a068-52e8cbd69115", "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": "ab973ab2-72aa-4a57-9132-b46002738489", "metadata": {}, "source": [ "Gerar distribuições de quase-probabilidade completas, com mitigação de erros, a partir de amostras das saídas de circuitos quânticos. Aproveite os recursos do Sampler para algoritmos de pesquisa e classificação, como o Grover e o QVSM.\n", "\n", "\n", "\n", "## Executar um único experimento\n", "\n", "Use o Sampler para retornar o resultado da medição como sequências de bits ou contagens de um único circuito.\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "fffb9af3-e122-4ca9-93e3-79edd6112ff8", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " > First ten results: ['1111010010110011001010101100010100001010110000100110111000000000100011100000001101110110001010000100000000010000000011000110101', '1001001111111001011011011011001010100101010000001101000010101101010000011100000000100100010000001000010000001010001001010101111', '0100001101111001110000000001000101101010001000010110111100011000100000010101101110001000010001111110001000100010011110000001100', '1000101001100101010000100001000101101010000011001110101111100010111011010110001010101010011011000001100000000010100100010100111', '1100011110101010000000011000100000100001110101011011100011011000111111110010000101000000000101011100001000100101000000000100001', '0000001100000000101100000000110100101011110100101101100110000000100110001110100000010010100000011101011001000000001011000100101', '1000001100110111001110100011101000111111101100110011100000000000000100001000100101100110000000100101000101001001110000001110000', '1100001000101000101100010011010101001010110010101000110111010100000100000011110000110011010110011010110010000000000000000000101', '0111010011101111010010000011010010001000000000010100000001001010001111100000100101000101000111110010101010100000101000100101011', '0100001000101010110010100111110100101001011111000011111010100110011000100001100000111101100101000000010010010000011110001011000']\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 random_hermitian\n", "from qiskit_ibm_runtime import QiskitRuntimeService, SamplerV2 as Sampler\n", "\n", "n_qubits = 127\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", "circuit.measure_all()\n", "\n", "pm = generate_preset_pass_manager(backend=backend, optimization_level=1)\n", "isa_circuit = pm.run(circuit)\n", "\n", "sampler = Sampler(backend)\n", "job = sampler.run([isa_circuit])\n", "result = job.result()\n", "\n", "# Get results for the first (and only) PUB\n", "pub_result = result[0]\n", "\n", "print(f\" > First ten results: {pub_result.data.meas.get_bitstrings()[:10]}\")" ] }, { "cell_type": "markdown", "id": "6b1cd3d9-8487-4889-9cd6-638a13fc7127", "metadata": {}, "source": [ "\n", "\n", "## Execute várias experiências em uma única tarefa\n", "\n", "Use o Sampler para retornar os resultados das medições como sequências de bits ou contagens de vários circuitos em uma única tarefa.\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "4f68f509-7965-41f7-9f5e-7922a45ba22d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " > First five results for pub 0: ['0101000101101010001010110001000101011010001000011001101011100011010001000000011001010110011001100000010001000001000100001111011', '0001010011100000101110011011110001110001000101000101011100101101010001000110101000001000101010000001000001000101101100000101000', '0100100110010110000000101011101100011000000101111110111111010001010000000010000010101110100101100111000101100010100111000010100', '1001011100111110000100011111110001011001100100001010000101010000111010000001100111110001101101001010110100000001010000010110000', '0001101101111010100001110101000011100001100001011101110100000110100001001101011110111011001011000101010110000010000111000001100']\n", " > First five results for pub 1: ['1111011001010000011101010001110000011000100000000001101010100000001100001001011010000100110100110111000001011000010010000000110', '0100111011011010011111101001110101101000100000000011111101011000100010000001110110010011000111010000100010101001011001001101110', '0110001000111101001000101000101000010010100010010000011011110001111010000000011010100000110000010000111101000010001001000100100', '1110110111110000010111101000111000100011110110011001100011000101000111110001001010000110100000011001100011101100000000000101010', '1000010011110101101101111100011000100101001011110010000101011100010111101100111001101111111111010100011010110100011000100100001']\n", " > First five results for pub 2: ['0100010111001111010001100100111010110000001000110000111010111001000011101110000110110010010000001000100100000010101000000001100', '0000110110001001100000001000101000001101010100011010111000101011011110000101011010000110000000000100000001010110010010000000001', '0001000111100100110100101100011011000010100001000100100000010001101110000000010101100111100100111101010111111001100101100010010', '0011110010110101011001111100000101000010001111000101100000001100110011111000000100010010101110111001011100000000001010000001001', '1110000001000100001010011100110100011110101010100100111111000110010111000001001100111000101001001100011011010010111000101000011']\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 random_hermitian\n", "from qiskit_ibm_runtime import QiskitRuntimeService, SamplerV2 as Sampler\n", "\n", "n_qubits = 127\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", "circuits = [iqp(mat) for mat in mats]\n", "for circuit in circuits:\n", " circuit.measure_all()\n", "\n", "pm = generate_preset_pass_manager(backend=backend, optimization_level=1)\n", "isa_circuits = pm.run(circuits)\n", "\n", "sampler = Sampler(mode=backend)\n", "job = sampler.run(isa_circuits)\n", "result = job.result()\n", "\n", "for idx, pub_result in enumerate(result):\n", " print(\n", " f\" > First five results for pub {idx}: \"\n", " f\"{pub_result.data.meas.get_bitstrings()[:5]}\"\n", " )" ] }, { "cell_type": "markdown", "id": "f6298621-8160-4e8b-8f43-316c5e388dd0", "metadata": {}, "source": [ "\n", "\n", "## Executar circuitos parametrizados\n", "\n", "Execute várias experiências em uma única tarefa, utilizando valores de parâmetros para aumentar a reutilização dos circuitos.\n", "\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "b8633a12-3cbc-42a2-85db-4ea1a2d7bda7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " >> First five results for the meas output register: ['1001010001111101000000100001100110000001110001011100011001110011101111001110110100110101011001100100011001110001110011011100011', '1000101001000011110100010010001111101110000001111100001010100000100000100110101111110011000000111001010100110001011011101001111', '0110111100011101011000100011000011000010110110000100101100010101111001101011111110011111100000100011111001101101001111011110101', '0110111011101011011111000100000011110011010000010000100110000011101000111100011100100110111000110100111000101011111100010100111', '0000001110100110101011011110110011111100011111001011010101111100000010111110010100001110001001110000001011110011001001000001111']\n" ] } ], "source": [ "import numpy as np\n", "from qiskit.circuit.library import real_amplitudes\n", "from qiskit.transpiler import generate_preset_pass_manager\n", "from qiskit_ibm_runtime import QiskitRuntimeService, SamplerV2 as Sampler\n", "\n", "n_qubits = 127\n", "\n", "service = QiskitRuntimeService()\n", "backend = service.least_busy(\n", " operational=True, simulator=False, min_num_qubits=n_qubits\n", ")\n", "\n", "# Step 1: Map classical inputs to a quantum problem\n", "circuit = real_amplitudes(num_qubits=n_qubits, reps=2)\n", "circuit.measure_all()\n", "\n", "# Define three sets of parameters for the circuit\n", "rng = np.random.default_rng(1234)\n", "parameter_values = [\n", " rng.uniform(-np.pi, np.pi, size=circuit.num_parameters) for _ in range(3)\n", "]\n", "\n", "# Step 2: Optimize problem for quantum execution.\n", "\n", "pm = generate_preset_pass_manager(backend=backend, optimization_level=1)\n", "isa_circuit = pm.run(circuit)\n", "\n", "# Step 3: Execute using IBM Quantum primitives.\n", "sampler = Sampler(backend)\n", "job = sampler.run([(isa_circuit, parameter_values)])\n", "result = job.result()\n", "# Get results for the first (and only) PUB\n", "pub_result = result[0]\n", "# Get counts from the classical register \"meas\".\n", "print(\n", " f\" >> First five results for the meas output register: \"\n", " f\"{pub_result.data.meas.get_bitstrings()[:5]}\"\n", ")" ] }, { "cell_type": "markdown", "id": "06bf761a-e9b7-47ca-911e-01b126090466", "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": "536ac5b5-00cf-42bf-a114-28144008d744", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " > The first five measurement results of job 1: ['1001001101100100001000001111101111001011010010010110110001110000000101010010001101001111000010110010101011001110110111001000100', '0100000111100101000010001110100001000011000011010000100001011000001001010111110100010000111101011100000100001110010110110001010', '1100011001000001101101000000000111001011110101110100001001000001001001100000101010010000000000110011000000011010011011100001111', '0011111111110001010010101111110111000010100001010000011101100010011011110001001000001100101000010100101010100010001001010001010', '1001111101110101010101110110011101111010011101000101110100011011110100000100100100110001001110101000000100101001001111000001010']\n" ] } ], "source": [ "import numpy as np\n", "from qiskit.circuit.library import iqp\n", "from qiskit.quantum_info import random_hermitian\n", "from qiskit.transpiler import generate_preset_pass_manager\n", "from qiskit_ibm_runtime import Batch, SamplerV2 as Sampler\n", "from qiskit_ibm_runtime import QiskitRuntimeService\n", "\n", "n_qubits = 127\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", "circuit.measure_all()\n", "mat = np.real(random_hermitian(n_qubits, seed=rng))\n", "another_circuit = iqp(mat)\n", "another_circuit.measure_all()\n", "\n", "pm = generate_preset_pass_manager(backend=backend, optimization_level=1)\n", "isa_circuit = pm.run(circuit)\n", "another_isa_circuit = pm.run(another_circuit)\n", "\n", "# The context manager automatically closes the batch.\n", "with Batch(backend=backend) as batch:\n", " sampler = Sampler(mode=batch)\n", " job = sampler.run([isa_circuit])\n", " another_job = sampler.run([another_isa_circuit])\n", " result = job.result()\n", " another_result = another_job.result()\n", "\n", "# first job\n", "\n", "print(\n", " f\" > The first five measurement results of job 1: \"\n", " f\"{result[0].data.meas.get_bitstrings()[:5]}\"\n", ")" ] }, { "cell_type": "code", "execution_count": 5, "id": "44b4c746-232b-4876-87c4-002cf6d11db7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " > The first five measurement results of job 2: ['1111111110000001000111010010010101010010111001110111001000100000010011101110101101001010001010000000000100011000010001000010000', '1110011100110100100100111001000101010011110001010110100100001110010010011100000000000100000010001001010100011110010000001011100', '1111101001010011110011011010000111000010001101100101000100000110000011001110001101100100100100100010011100001000000000100111010', '1100010101000011101000110100000101001000110110010100000000001000010110100110000111010101010010001101010010100000100111010110000', '1010100100100110011100010010100000101101101101000111000010101110010111010100001111000001100010100011110000000011101000101001100']\n" ] } ], "source": [ "# second job\n", "print(\n", " \" > The first five measurement results of job 2:\",\n", " another_result[0].data.meas.get_bitstrings()[:5],\n", ")" ] }, { "cell_type": "markdown", "id": "d5c423e4-7586-4fdf-8452-3c62a705c955", "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" } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" 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