{ "cells": [ { "cell_type": "markdown", "id": "frontmatter", "metadata": {}, "source": [ "---\n", "title: \"Schnellstart\"\n", "description: \"Schnellstart für die neueste Version von Qiskit zur Propagationsgeräuschunterdrückung\"\n", "---\n", "\n" ] }, { "cell_type": "markdown", "id": "0b3182b6", "metadata": {}, "source": [ "\n", "\n", "# Schnellstart\n", "\n", "Diese Anleitung zeigt ein minimales, funktionsfähiges Beispiel für das `qiskit-addon-pna` Paket. Wir verwenden die propagierte Schallabsorption (PNA), um eine lärmmindernde Beobachtable zu erstellen. Ausgehend von einer Schaltung und einem Pauli-Lindblad-Rauschmodell überträgt die PNA die Observable klassisch über den inversen Rauschkanal. Durch die Messung der resultierenden Observable auf der verrauschten QPU wird das gelernte Gatterrauschen gemildert.\n", "\n", "Um zu erfahren, wie man einen realistischen Workflow aufbaut und diesen auf Quantenhardware mit dem [„Directed Execution Model“](/docs/guides/directed-execution-model) ausführt – einschließlich des Trainings des Rauschmodells mit `NoiseLearnerV3`–, sehen Sie sich das [PNA-Tutorial](/docs/tutorials/propagated-noise-absorption) auf der „ IBM Quantum Platform “ an.\n", "\n" ] }, { "cell_type": "markdown", "id": "a4068f26", "metadata": {}, "source": [ "\n", "\n", "## 1. Bereiten Sie die Eingaben für PNA vor\n", "\n", "PNA nimmt als Eingabe eine Schaltung, ein Rauschmodell und eine Beobachtungsgröße entgegen. Hier bauen wir ein 10-Qubit-Trotter-Ising-Modell mit transversalen Feldern auf einer „ 1D “-Kette auf. Wir generieren für jedes Verschränkungsgatter ein zufälliges 2-lokales Pauli-Lindblad-Rauschmodell und binden es als Qiskit-Aer-Befehl `PauliLindbladError` unmittelbar vor diesem Gatter ein. Wir wählen eine weight-4 Pauli-Z-Beobachtungsgröße zur Messung aus.\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "4a9a3ced", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\"Output" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "from qiskit import QuantumCircuit\n", "from qiskit.quantum_info import SparsePauliOp, pauli_basis\n", "from qiskit_aer.noise import PauliLindbladError\n", "\n", "\n", "def random_pauli_lindblad_noise(generators, seed, noise_scale=2e-3):\n", " rates = np.random.default_rng(seed).random(len(generators)) * noise_scale\n", " return PauliLindbladError(generators, rates)\n", "\n", "\n", "def ising_circuit(\n", " num_qubits,\n", " layers,\n", " edge_noise=None,\n", " *,\n", " num_steps=3,\n", " rx_angle=np.pi / 8,\n", " rzz_angle=-np.pi / 2,\n", "):\n", " \"\"\"Trotterized transverse-field Ising model; edge_noise=None gives the noiseless circuit.\"\"\"\n", " qc = QuantumCircuit(num_qubits)\n", " for _ in range(num_steps):\n", " qc.rx(rx_angle, range(num_qubits))\n", " for layer in layers:\n", " for edge in layer:\n", " if edge_noise is not None:\n", " qc.append(\n", " edge_noise[edge], edge\n", " ) # inject synthetic gate noise\n", " qc.rzz(rzz_angle, *edge)\n", " return qc\n", "\n", "\n", "num_qubits = 10\n", "\n", "# Two entangling layers per Trotter step: even and odd bonds of a 1D chain\n", "layers = [\n", " [(i, i + 1) for i in range(0, num_qubits - 1, 2)],\n", " [(i, i + 1) for i in range(1, num_qubits - 1, 2)],\n", "]\n", "edges = [edge for layer in layers for edge in layer]\n", "\n", "# Random 2-local Pauli-Lindblad noise, one instance per entangling gate\n", "two_qubit_paulis = SparsePauliOp(\n", " [p for p in pauli_basis(2) if np.sum(p.x + p.z)]\n", ").paulis\n", "edge_noise = {\n", " edge: random_pauli_lindblad_noise(two_qubit_paulis, seed=1234 + j)\n", " for j, edge in enumerate(edges)\n", "}\n", "\n", "noisy_circuit = ising_circuit(num_qubits, layers, edge_noise)\n", "\n", "# A single weight-4 observable: \n", "observable = SparsePauliOp.from_sparse_list(\n", " [(\"ZZZZ\", [3, 4, 5, 6], 1.0)], num_qubits=num_qubits\n", ")\n", "\n", "noisy_circuit.draw(\"mpl\", fold=-1, scale=0.6)" ] }, { "cell_type": "markdown", "id": "b99617c3", "metadata": {}, "source": [ "\n", "\n", "## 2. Die rauschmindernde Observable generieren\n", "\n", "[„generate\\_noise\\_mitigating\\_observable“](/docs/api/qiskit-addon-pna/qiskit-addon-pna#generate_noise_mitigating_observable) leitet jeden Pauli-Generator des inversen Rauschkanals bis zum Ende der Schaltung weiter. Die Beobachtungsgröße wird dann durch den inversen Rauschkanal rückwärts propagiert, wodurch ein neuer Beobachtungswert $\\tilde{O}$ zurückgegeben wird. Drei Schlüsselparameter beeinflussen den Rechenaufwand:\n", "\n", "* `max_err_terms`: die Anzahl der Terme, die in jedem Anti-Rausch-Generator beibehalten werden, während die Berechnung vorwärts fortgesetzt wird.\n", "* `max_obs_terms`: die Anzahl der in „ $\\tilde{O}$ “ gespeicherten Begriffe.\n", "* `atol`: Terme, deren Koeffizient unter diesem Schwellenwert liegt, werden verworfen.\n", "\n", "`atol`Für diesen kleinen, an Clifford-Schaltungen angelehnten Schaltkreis legen wir die Termbegrenzung hoch fest und verwenden einen bescheidenen Wert, sodass $\\tilde{O}$ klein bleibt und wir alle seine Terme messen können.\n", "\n", "***Hinweis: Diese Funktion verwendet Python `multiprocessing`. Wenn Sie es als Skript ausführen, rufen Sie es innerhalb eines `if __name__ == \"__main__\":` `guard` auf.***\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "39891ec7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Original observable: 1 term\n", "Noise-mitigating observable: 207 terms\n" ] } ], "source": [ "from qiskit_addon_pna import generate_noise_mitigating_observable\n", "\n", "mitigating_observable = generate_noise_mitigating_observable(\n", " noisy_circuit,\n", " observable,\n", " max_err_terms=100_000,\n", " max_obs_terms=100_000,\n", " atol=1e-5,\n", " num_processes=4,\n", ")\n", "\n", "print(f\"Original observable: {len(observable)} term\")\n", "print(f\"Noise-mitigating observable: {len(mitigating_observable)} terms\")" ] }, { "cell_type": "markdown", "id": "f7881a9e", "metadata": {}, "source": [ "\n", "\n", "## 3. Gate-Fehler durch Messung der rauschmindernden Observable minimieren\n", "\n", "Hier sehen wir, dass die neue Observable das das Experiment beeinträchtigende Gate-Rauschen wirksam mindert.\n", "\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "a56dd204", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ideal (noiseless): 0.8073\n", "Noisy (unmitigated): 0.6431\n", "Mitigated (PNA): 0.8071\n" ] }, { "data": { "text/plain": [ "\"Output" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "from qiskit_aer.primitives import EstimatorV2\n", "\n", "noiseless_circuit = ising_circuit(num_qubits, layers)\n", "\n", "# density_matrix method at zero precision -> exact expectation values (no shot noise)\n", "estimator = EstimatorV2(\n", " options={\n", " \"backend_options\": {\"method\": \"density_matrix\"},\n", " \"default_precision\": 0.0,\n", " }\n", ")\n", "\n", "ideal, noisy, mitigated = (\n", " result.data.evs\n", " for result in estimator.run(\n", " [\n", " (noiseless_circuit, observable),\n", " (noisy_circuit, observable),\n", " (noisy_circuit, mitigating_observable),\n", " ]\n", " ).result()\n", ")\n", "\n", "print(f\"Ideal (noiseless): {ideal:.4f}\")\n", "print(f\"Noisy (unmitigated): {noisy:.4f}\")\n", "print(f\"Mitigated (PNA): {mitigated:.4f}\")\n", "\n", "fig, ax = plt.subplots()\n", "ax.bar(\n", " [\"Noisy\", \"Mitigated\"],\n", " [noisy, mitigated],\n", " width=0.6,\n", " color=[\"#b0b0b0\", \"#4c4c4c\"],\n", ")\n", "ax.axhline(ideal, color=\"green\", linestyle=\"--\", label=\"Ideal (noiseless)\")\n", "ax.set_ylabel(r\"$\\langle Z_3 Z_4 Z_5 Z_6 \\rangle$\")\n", "ax.legend()\n", "plt.show()" ] }, { "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": 5 }