{ "cells": [ { "cell_type": "markdown", "id": "008d2ceb-f6fa-42f6-a7df-6bd604775278", "metadata": {}, "source": [ "---\n", "title: Get started with the Sampler primitive\n", "description: How to use the Sampler primitive in Qiskit Runtime.\n", "---\n", "\n", "# Get started with the Sampler primitive\n", "\n" ] }, { "cell_type": "markdown", "id": "b7e96291-0925-4d7f-81a8-a7738549477c", "metadata": {}, "source": [ "The Sampler's core task is sampling the output register from the execution of one or more quantum circuits. [Dynamic circuits](/docs/guides/execute-dynamic-circuits) and parameterized circuits are accepted as input (if parametrized circuits are submitted, the parameter values must also be provided). Sampler also supports built-in dynamical decoupling and twirling for [error suppression](/docs/guides/error-mitigation-and-suppression-techniques).\n", "\n", "The steps in this topic describe how to set up Sampler, explore the options you can use to configure it, and invoke it in a program.\n", "\n" ] }, { "cell_type": "markdown", "id": "818a2b3d-3950-4a28-8e37-39959c56484b", "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", " Package versions\n", "\n", " The code on this page was developed using the following requirements.\n", " We recommend using these versions or newer.\n", "\n", " ```\n", " qiskit[all]~=2.3.0\n", " qiskit-ibm-runtime~=0.43.1\n", " ```\n", "
\n", "\n" ] }, { "cell_type": "markdown", "id": "64e2e09f-8528-4088-897b-1529b451ab1e", "metadata": {}, "source": [ "## Steps to use the Sampler primitive\n", "\n", "### 1. Initialize the account\n", "\n", "Because Qiskit Runtime is a managed service, you first need to initialize your account. You can then select the QPU you want to use to calculate the expectation value.\n", "\n", "Follow the steps in the [Set up your IBM Cloud account](/docs/guides/cloud-setup) topic if you don't already have an account set up.\n", "\n", "\n", " To use the newly supported [fractional gates](/docs/guides/fractional-gates), set `use_fractional_gates=True` when requesting a backend from a `QiskitRuntimeService` instance. For example:\n", "\n", " ```python\n", " service = QiskitRuntimeService()\n", " fractional_gate_backend = service.least_busy(use_fractional_gates=True)\n", " ```\n", "\n", " This is an experimental feature and might change in the future.\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 7, "id": "b40504d7-aee5-4b30-98b1-265e70bece8d", "metadata": {}, "outputs": [], "source": [ "from qiskit_ibm_runtime import QiskitRuntimeService\n", "\n", "service = QiskitRuntimeService()\n", "backend = service.least_busy(\n", " operational=True, simulator=False, min_num_qubits=127\n", ")" ] }, { "cell_type": "markdown", "id": "73374fbc-d3b6-4d2d-84c6-edf85b43ea25", "metadata": {}, "source": [ "### 2. Create a circuit\n", "\n", "You need at least one circuit as the input to the Sampler primitive.\n", "\n" ] }, { "cell_type": "code", "execution_count": 8, "id": "dfe23a34-2ea9-48af-bd1d-c7e3185aa80c", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from qiskit.circuit.library import efficient_su2\n", "\n", "circuit = efficient_su2(127, entanglement=\"linear\")\n", "circuit.measure_all()\n", "# The circuit is parametrized, so we will define the parameter values for execution\n", "param_values = np.random.rand(circuit.num_parameters)" ] }, { "cell_type": "markdown", "id": "6cf08ef6-34d3-42e6-8cb3-391b60217289", "metadata": {}, "source": [ "The circuit and observable need to be transformed to only use instructions supported by the QPU (referred to as *instruction set architecture (ISA)* circuits). Use the transpiler to do this.\n", "\n" ] }, { "cell_type": "code", "execution_count": 9, "id": "062bd89b-b13e-46d0-96b6-6c84b2131415", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ">>> Circuit ops (ISA): OrderedDict([('sx', 3089), ('rz', 3036), ('cz', 1092), ('measure', 127), ('barrier', 1)])\n" ] } ], "source": [ "from qiskit.transpiler import generate_preset_pass_manager\n", "\n", "pm = generate_preset_pass_manager(optimization_level=1, backend=backend)\n", "isa_circuit = pm.run(circuit)\n", "print(f\">>> Circuit ops (ISA): {isa_circuit.count_ops()}\")" ] }, { "cell_type": "markdown", "id": "cf0f2c0a-8574-45c6-a43b-1a99eac81279", "metadata": {}, "source": [ "### 3. Initialize the Qiskit Runtime Sampler\n", "\n", "When you initialize Sampler, use the `mode` parameter to specify the mode you want it to run in. Possible values are `batch`, `session`, or `backend` objects for batch, session, and job execution mode, respectively. For more information, see [Introduction to Qiskit Runtime execution modes.](execution-modes) Note that Open Plan users cannot submit session jobs.\n", "\n" ] }, { "cell_type": "code", "execution_count": 10, "id": "a2b80dca-dff8-49f9-8154-e2cb0b768507", "metadata": {}, "outputs": [], "source": [ "from qiskit_ibm_runtime import SamplerV2 as Sampler\n", "\n", "sampler = Sampler(mode=backend)" ] }, { "cell_type": "markdown", "id": "f35972bf-17d3-40be-852b-9e56615c7c3c", "metadata": {}, "source": [ "### 4. Invoke Sampler and get results\n", "\n", "Next, invoke the `run()` method to generate the output. The circuit and optional parameter value sets are input as *primitive unified bloc* (PUB) tuples.\n", "\n" ] }, { "cell_type": "code", "execution_count": 11, "id": "e52e6a96-dc23-4f76-8152-b54514a99dfb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ">>> Job ID: d5k96rsjt3vs73ds5tig\n", ">>> Job Status: QUEUED\n" ] } ], "source": [ "job = sampler.run([(isa_circuit, param_values)])\n", "print(f\">>> Job ID: {job.job_id()}\")\n", "print(f\">>> Job Status: {job.status()}\")" ] }, { "cell_type": "code", "execution_count": 12, "id": "4543fac5-abdc-4440-a1a2-d32aabe135d6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "First ten results for the 'meas' output register: ['0101001101010000011001110001011000010010001100001000100110011111011110000010110001101000110011101010000100011011000110101111000', '0100111000000100110001100100000101111000111001101000110111101110110010010100001101001111001010011101010000010011000110000010001', '0101111101111111010011010101000000110100000010000010011101100011100011001100000100100001000101000000100001010101010011001101100', '1100110101111111001110010000010100101010101010001000001100100110011111010000000010001000110111010000010101100000100000110111001', '0010000001111001111010100100010111101000101000100000101100001000011100000100011010110110100011100110001001110110111101010011000', '0101110000001000100100010010100100111000010100000000010010000000010110010010000110000001110110010100000111001110100100111101100', '0100011111101001000111110011011101101101110101110001010111011101111110011101001000000001110000011110000101010000001010000100000', '0001010101011000110100000100111111100001011000111110000011000111001101010000010001001100000110000000100000110101010010101110010', '0100011010001110011110000110100101100100101001001111010100100101010100010000000010100000101010110010000000001000010101011111110', '0000011000000111000001000101111111110110101100110000001100010010011101011100001010000100011010001010001101000000000000010001001']\n" ] } ], "source": [ "result = job.result()\n", "\n", "# Get results for the first (and only) PUB\n", "pub_result = result[0]\n", "print(\n", " f\"First ten results for the 'meas' output register: {pub_result.data.meas.get_bitstrings()[:10]}\"\n", ")" ] }, { "cell_type": "markdown", "id": "d38dd409-e0d8-4749-bb22-58ae9a53d26a", "metadata": {}, "source": [ "## Next steps\n", "\n", "\n", " * Learn how to [test locally](local-testing-mode) before running on quantum computers.\n", " * Review detailed [examples](sampler-examples).\n", " * Practice with primitives by working through the [Cost function lesson](/learning/courses/variational-algorithm-design/cost-functions) in IBM Quantum Learning.\n", " * Learn how to transpile locally in the [Transpile](transpile/) section.\n", " * Try the [Compare transpiler settings](/docs/guides/circuit-transpilation-settings#compare-transpiler-settings) guide.\n", " * Learn how to [use the primitive options](/docs/guides/runtime-options-overview).\n", " * View the API for [Sampler](/docs/api/qiskit-ibm-runtime/options-sampler-options) options.\n", " * Read [Migrate to V2 primitives](/docs/guides/v2-primitives).\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "id": "a1b8767d", "source": "© IBM Corp., 2017-2026" } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3" } }, "nbformat": 4, "nbformat_minor": 4 }