{ "cells": [ { "cell_type": "markdown", "id": "7e6b2936-3a18-4917-9c47-30e2d4ce5775", "metadata": {}, "source": [ "---\n", "title: \"Visualizar resultados\"\n", "description: \"Trazar los resultados de la ejecución del circuito cuántico utilizando Qiskit\"\n", "---\n", "\n", "\n", "\n", "# Visualizar resultados\n", "\n" ] }, { "cell_type": "markdown", "id": "06a4399f-bca6-4c2e-9128-1494017d0249", "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", " El código de esta página se ha desarrollado teniendo en cuenta los siguientes requisitos.\n", " Recomendamos utilizar estas versiones o versiones más recientes.\n", "\n", " ```\n", " qiskit[all]~=2.5.0\n", " ```\n", " \n", "\n", "\n" ] }, { "cell_type": "markdown", "id": "e8d0138c-fd97-49c2-88a5-1929a5a09258", "metadata": {}, "source": [ "\n", "\n", "## Histograma de trama\n", "\n", "La función `plot_histogram` visualiza el resultado del muestreo de un circuito cuántico en una QPU.\n", "\n", "\n", " Esta función devuelve un objeto `matplotlib.Figure` . Cuando la última línea de una celda de código produce estos objetos, los cuadernos Jupyter los muestran debajo de la celda. Si llama a estas funciones en otros entornos o en scripts, tendrá que mostrar o guardar explícitamente las salidas.\n", "\n", " Hay dos opciones:\n", "\n", " * Llame a `.show()` en el objeto devuelto para abrir la imagen en una nueva ventana (suponiendo que su backend matplotlib configurado es interactivo).\n", " * Llame a `.savefig(\"out.png\")` para guardar la figura en `out.png` en el directorio de trabajo actual. El método `savefig()` toma una ruta para que pueda ajustar la ubicación y el nombre del archivo donde guardará la salida. Por ejemplo, `plot_state_city(psi).savefig(\"out.png\")`.\n", "\n", "\n", "Por ejemplo, hacer un estado Bell de dos qubits:\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "5cf67f92-a86d-496d-9e13-0d8a841c8dfa", "metadata": { "tags": [ "ignore-warnings" ] }, "outputs": [], "source": [ "from qiskit.primitives import StatevectorSampler as Sampler\n", "from qiskit.transpiler import generate_preset_pass_manager\n", "\n", "from qiskit.circuit import QuantumCircuit\n", "from qiskit.visualization import plot_histogram" ] }, { "cell_type": "code", "execution_count": 2, "id": "938e8206-d7e8-447d-b798-b7c2507f8901", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PrimitiveResult([SamplerPubResult(data=DataBin(meas=BitArray()), metadata={'shots': 1024, 'circuit_metadata': {}})], metadata={'version': 2})\n" ] } ], "source": [ "# Quantum circuit to make a Bell state\n", "bell = QuantumCircuit(2)\n", "bell.h(0)\n", "bell.cx(0, 1)\n", "bell.measure_all()\n", "\n", "pm = generate_preset_pass_manager(optimization_level=1)\n", "isa_circuit = pm.run(bell)\n", "\n", "# execute the quantum circuit\n", "sampler = Sampler()\n", "job = sampler.run([isa_circuit])\n", "result = job.result()\n", "\n", "print(result)" ] }, { "cell_type": "code", "execution_count": 3, "id": "57d8053e-d030-460d-9c1f-772e53b1a49b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\"Output" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plot_histogram(result[0].data.meas.get_counts())" ] }, { "cell_type": "markdown", "id": "e3a68e3d-21e6-45a0-bc40-f9a2214dd5b3", "metadata": {}, "source": [ "\n", "\n", "### Opciones al trazar un histograma\n", "\n", "Utilice las siguientes opciones de `plot_histogram` para ajustar el gráfico de salida.\n", "\n", "* `legend`: Proporciona una etiqueta para las ejecuciones. Toma una lista de cadenas utilizadas para etiquetar los resultados de cada ejecución. Esto es útil sobre todo cuando se trazan múltiples resultados de ejecución en el mismo histograma\n", "* `sort`: Ajusta el orden de las barras en el histograma. Puede establecerse en orden ascendente con `asc` o descendente con `desc`\n", "* `number_to_keep`: Toma un número entero para el número de términos a mostrar. El resto se agrupan en una única barra denominada \"descanso\"\n", "* `color`: Ajusta el color de las barras; toma una cadena o una lista de cadenas para los colores a utilizar para las barras en cada ejecución\n", "* `bar_labels`: Ajusta si las etiquetas se imprimen por encima de las barras\n", "* `figsize`: Toma una tupla del tamaño en pulgadas para hacer la cifra de salida\n", "\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "bd70e13f-5c52-42fb-8dde-980b15e3604a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\"Output" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Execute two-qubit Bell state again\n", "\n", "job = sampler.run([isa_circuit], shots=1000)\n", "second_result = job.result()\n", "\n", "# Plot results with custom options\n", "plot_histogram(\n", " [\n", " result[0].data.meas.get_counts(),\n", " second_result[0].data.meas.get_counts(),\n", " ],\n", " legend=[\"first\", \"second\"],\n", " sort=\"desc\",\n", " figsize=(15, 12),\n", " color=[\"orange\", \"black\"],\n", " bar_labels=False,\n", ")" ] }, { "cell_type": "markdown", "id": "019ee04e-0730-4536-94cc-7e2b50d921e1", "metadata": {}, "source": [ "\n", "\n", "## Resultados del estimador de parcelas\n", "\n", "Qiskit no tiene una función incorporada para trazar los resultados del Estimador, pero puede utilizar Matplotlib 's [`bar` plot](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.bar.html) para una visualización rápida.\n", "\n", "Para demostrarlo, la siguiente celda estima los valores de expectativa de siete observables diferentes sobre un estado cuántico.\n", "\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "17c9893a-d1bf-4726-b444-6dce1d56805f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PubResult(data=DataBin(evs=np.ndarray(), stds=np.ndarray(), shape=(7, 1)), metadata={'target_precision': 0.0, 'circuit_metadata': {}})\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\"Output" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "from qiskit import QuantumCircuit\n", "from qiskit.quantum_info import SparsePauliOp\n", "from qiskit.primitives import StatevectorEstimator as Estimator\n", "from qiskit.transpiler import generate_preset_pass_manager\n", "from matplotlib import pyplot as plt\n", "\n", "# Simple estimation experiment to create results\n", "qc = QuantumCircuit(2)\n", "qc.h(0)\n", "qc.crx(1.5, 0, 1)\n", "\n", "observables_labels = [\"ZZ\", \"XX\", \"YZ\", \"ZY\", \"XY\", \"XZ\", \"ZX\"]\n", "observables = [SparsePauliOp(label) for label in observables_labels]\n", "\n", "pm = generate_preset_pass_manager(optimization_level=1)\n", "isa_circuit = pm.run(qc)\n", "isa_observables = [\n", " operator.apply_layout(isa_circuit.layout) for operator in observables\n", "]\n", "\n", "# Reshape observable array for broadcasting\n", "reshaped_ops = np.fromiter(isa_observables, dtype=object)\n", "reshaped_ops = reshaped_ops.reshape((7, 1))\n", "\n", "estimator = Estimator()\n", "job = estimator.run([(isa_circuit, reshaped_ops)])\n", "result = job.result()[0]\n", "exp_val = job.result()[0].data.evs\n", "print(result)\n", "\n", "# Since the result array is structured as a 2D array where each element is a\n", "# list containing a single value, you need to flatten the array.\n", "\n", "# Plot using Matplotlib\n", "plt.bar(observables_labels, exp_val.flatten())" ] }, { "cell_type": "markdown", "id": "a520f049-c2ee-4f14-8039-b5be671f25ae", "metadata": {}, "source": [ "La siguiente celda utiliza el [error estándar](https://en.wikipedia.org/wiki/Standard_error) estimado de cada resultado y los añade como barras de error. Consulte la [documentación de la trama `bar` ](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.bar.html) para obtener una descripción completa de la trama.\n", "\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "4eb79f4b-36b5-4797-a1a0-67d881d46ca4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Expectation values (with standard errors)')" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "\"Output" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "standard_error = job.result()[0].data.stds\n", "\n", "_, ax = plt.subplots()\n", "ax.bar(\n", " observables_labels,\n", " exp_val.flatten(),\n", " yerr=standard_error.flatten(),\n", " capsize=2,\n", ")\n", "ax.set_title(\"Expectation values (with standard errors)\")" ] }, { "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 }