{ "cells": [ { "cell_type": "markdown", "id": "d0e7f54f-e951-44d4-8cd8-5b539cf5c91c", "metadata": {}, "source": [ "---\n", "title: \"実行者の例\"\n", "description: \"qiskit-ibm-runtime における Executor プリミティブの使用例。\"\n", "---\n", "\n", "\n", "\n", "# 実行者の例\n", "\n" ] }, { "cell_type": "markdown", "id": "a53ccd93-5bca-4dfb-a8a0-fcf4ed046fa7", "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", " このページのコードは、以下の要件に基づいて開発されました。\n", " これらのバージョン以降のご利用をお勧めします。\n", "\n", " ```\n", " qiskit[all]~=2.4.0\n", " qiskit-ibm-runtime~=0.46.1\n", " samplomatic~=0.18.0\n", " ```\n", " \n", "\n", "\n" ] }, { "cell_type": "markdown", "id": "fed832ee-87a7-4261-874d-84002f3863b5", "metadata": {}, "source": [ "このセクションの例では、Executorプリミティブの一般的な使用方法をいくつか紹介します。 これらの例を実行する前に、 「[Qiskit](/docs/guides/install-qiskit) と [Executor のクイックスタート](/docs/guides/directed-execution-model) 」の手順に従ってください。\n", "\n", "\n", "\n", "## 開始前に\n", "\n", "`samplex`このページにあるコード例の一部では、Samplomatic パッケージに含まれる を使用しています。 したがって、これらのコードブロックを実行する前に、次のコードブロックに示すように、Samplomaticをインストールする必要があります。 詳細については、 [Samplomaticのドキュメント](https://qiskit.github.io/samplomatic)を参照してください。\n", "\n", "```python\n", "pip install samplomatic\n", "\n", "# For visualization support, include the visualization dependencies.\n", "# pip install samplomatic[vis]\n", "```\n", "\n" ] }, { "cell_type": "markdown", "id": "19549d62-4095-458d-a691-00b9bc456ed5", "metadata": {}, "source": [ "\n", "\n", "## 例：パラメータ化された回路\n", "\n", "この例では、パラメータ付き回路アイテムの追加方法と、サンプレックス・アイテムの追加方法について説明します。 手順は以下の通りです：\n", "\n", "1. 回路の設定：ターゲット回路を生成し、トランスパイルします。\n", "2. サンプレックスの準備：ゲートと測定項目を注釈付きのボックスにまとめ、回路テンプレートとサンプレックスのペアを生成します。\n", "3. 実行：回路アイテムとサンプレックスアイテムを へ追加し、 `QuantumProgram` 両方を単一のジョブで実行します。\n", "\n", "\n", "\n", "### 回路を組み立てる\n", "\n", "3量子ビットのGHZ状態を準備し、量子ビットをパウリZ軸を中心に回転させ、計算基底で量子ビットを測定する。\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "caf43d3e-ed55-4805-8d19-6c0980eeb1dc", "metadata": {}, "outputs": [], "source": [ "from qiskit.circuit import Parameter, QuantumCircuit\n", "from qiskit_ibm_runtime import QiskitRuntimeService, Executor\n", "from qiskit_ibm_runtime.quantum_program import QuantumProgram\n", "from qiskit.transpiler import generate_preset_pass_manager\n", "import numpy as np\n", "from samplomatic import build\n", "from samplomatic.transpiler import generate_boxing_pass_manager\n", "\n", "# Generate the circuit\n", "circuit = QuantumCircuit(3)\n", "circuit.h(0)\n", "circuit.h(1)\n", "circuit.cz(0, 1)\n", "circuit.h(1)\n", "circuit.h(2)\n", "circuit.cz(1, 2)\n", "circuit.h(2)\n", "circuit.rz(Parameter(\"theta\"), 0)\n", "circuit.rz(Parameter(\"phi\"), 1)\n", "circuit.rz(Parameter(\"lam\"), 2)\n", "circuit.measure_all()" ] }, { "cell_type": "markdown", "id": "f95bc53d-ed01-4b94-988b-1e497916d0fa", "metadata": {}, "source": [ "バックエンドを指定し、QPUがサポートする命令のみを使用するように回路をトランスパイルします（これは命令セットアーキテクチャ（ISA）回路と呼ばれます）。\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "bf633f01-372f-4519-b89d-ab4075255bd9", "metadata": {}, "outputs": [], "source": [ "# Initialize the service and choose a backend\n", "service = QiskitRuntimeService()\n", "backend = service.least_busy(operational=True, simulator=False)\n", "\n", "# Transpile the circuit to ISA\n", "preset_pass_manager = generate_preset_pass_manager(\n", " backend=backend, optimization_level=3\n", ")\n", "isa_circuit = preset_pass_manager.run(circuit)" ] }, { "cell_type": "markdown", "id": "fbecf32d-d1b9-4e12-ab56-0a1ede7ebf04", "metadata": {}, "source": [ "\n", "\n", "### サンプレックスの準備をする\n", "\n", "便利関数 `generate_boxing_pass_manager` とそのツイリングパラメータを使用して、2量子ビットゲートと測定をボックスにまとめ、ツイリング注釈を適用します。\n", "\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "8d6d64ad-12ee-4c0c-a683-d1178600d3c7", "metadata": {}, "outputs": [], "source": [ "boxing_pm = generate_boxing_pass_manager(\n", " # Add gate twirling\n", " enable_gates=True,\n", " # Add measurement twirling\n", " enable_measures=True,\n", ")\n", "\n", "boxed_circuit = boxing_pm.run(isa_circuit)" ] }, { "cell_type": "markdown", "id": "2e8fb1f9-4b15-4161-9f8f-1c24d64b0045", "metadata": {}, "source": [ "この `build` メソッドを使用して、テンプレート回路とサンプルを生成します。\n", "\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "90e21ca1-7f39-4636-b65e-28685b610a3c", "metadata": {}, "outputs": [], "source": [ "# Build the template circuit and the samplex\n", "template_circuit, samplex = build(boxed_circuit)" ] }, { "cell_type": "markdown", "id": "521a7263-8d2f-46fd-a261-c23ae56aa5b5", "metadata": {}, "source": [ "\n", "\n", "### サーキットトレーニングを行う\n", "\n", "Executorはオブジェクトを実行します `QuantumProgram` 。 それぞれ `QuantumProgram` に複数の項目を含めることができます。 この例では、実行用の回路項目とサンプレックス項目を追加します。 詳細については、 [「Executorの入力と出力」](/docs/guides/executor-input-output) を参照してください。\n", "\n", "まず、各アイテムの各設定についてショットを要求する `1024` 、空のプログラムを初期化します。\n", "\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "85d7d9ef-a74c-42e4-a758-0f490e29275d", "metadata": {}, "outputs": [], "source": [ "# Generate a quantum program\n", "program = QuantumProgram(shots=1024)" ] }, { "cell_type": "markdown", "id": "8869d18d-cc0e-4556-a195-cd6b590bcef7", "metadata": {}, "source": [ "`QuantumProgram`回路項目を.に追加する。 この回路アイテムは、ISA回路と、そのパラメータ値10セットの2つの部分で構成されています。\n", "\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "19963674-3e72-473c-b24a-228316946dc5", "metadata": {}, "outputs": [], "source": [ "# Append the circuit and the parameter values to the program\n", "program.append_circuit_item(\n", " isa_circuit,\n", " circuit_arguments=np.random.rand(10, 3), # 10 sets of parameter values\n", ")" ] }, { "cell_type": "markdown", "id": "41505f83-a7a6-4ea8-a7fc-a3fb45a2992a", "metadata": {}, "source": [ "samplex 項目を、以下の引数とともに に `QuantumProgram` 追加してください：\n", "\n", "* テンプレート回路と、関数 `build` によって生成されたサンプレックス\n", "* 元の回路のパラメータ値の10組\n", "* 実行する無作為化の回数\n", "\n" ] }, { "cell_type": "code", "execution_count": 7, "id": "d6b07700-5834-4baa-a08a-434516f5bc07", "metadata": {}, "outputs": [], "source": [ "# Append the template circuit and samplex as a samplex item\n", "program.append_samplex_item(\n", " template_circuit,\n", " samplex=samplex,\n", " samplex_arguments={\n", " \"parameter_values\": np.random.rand(\n", " 10, 3\n", " ), # 10 sets of parameter values\n", " },\n", " shape=(2, 14, 10),\n", ")" ] }, { "cell_type": "markdown", "id": "f39e3e78-5953-4801-b521-dd48ae487acf", "metadata": {}, "source": [ "\n", "\n", "### エグゼキュータ・ジョブを実行する\n", "\n" ] }, { "cell_type": "code", "execution_count": 8, "id": "149addb2-e76f-427c-bac4-54b6a83ddb0a", "metadata": {}, "outputs": [], "source": [ "# initialize an Executor with default options\n", "executor = Executor(mode=backend)\n", "\n", "# Submit the job\n", "job = executor.run(program)\n", "\n", "# Retrieve the result\n", "result = job.result()" ] }, { "cell_type": "markdown", "id": "93698fec-cff7-4153-8cbb-f7e39c972d9a", "metadata": {}, "source": [ "各タスクの結果を取得します。\n", "\n" ] }, { "cell_type": "code", "execution_count": 9, "id": "77235bf8-bcac-43fb-89b0-9ba74b976053", "metadata": {}, "outputs": [], "source": [ "# Access the results of the classical register of task #0, the CircuitItem\n", "result_0 = result[0][\"meas\"]\n", "\n", "# Access the results of the classical register of task #1, the SamplexItem\n", "result_1 = result[1][\"meas\"]" ] }, { "cell_type": "markdown", "id": "64063941-87b5-4c5d-bbf9-8920c24a216f", "metadata": {}, "source": [ "\n", "\n", "## 例：PECを実行する\n", "\n", "この例では、samplex アイテムを使用して、誤差低減のための確率的誤差相殺（ [PEC](/docs/guides/error-mitigation-and-suppression-techniques#pec) ）を行う方法を示します。\n", "\n", "10個の量子ビットと2つの異なる層からなるCXゲートを持つ回路の対称配置について考えてみよう。 主な業務内容は以下の通りです：\n", "\n", "* くるくると回しながらこの動きを行ってください。\n", "* 論文 [「ノイズの多い量子プロセッサにおける疎なパウリ・リンドブラッドモデルを用いた確率的誤差キャンセル」](https://arxiv.org/abs/2201.09866) と同様に、PEC緩和策を適用して回路を実行する。\n", "\n", "このパイプラインは、以下のステップで構成されています：\n", "\n", "1. 設定：対象回路を生成し、その演算をボックスにグループ化する。\n", "2. 学習：PECを用いて低減したい指令のノイズを学習します。\n", "3. 実行：バックエンド上で回路を実行する。\n", "4. 分析：結果の後処理と分析を行う。\n", "\n", "比較のために、このミラー回路を2回実行します。 1回目はパウリ回転のみを適用した場合、もう1回はPEC緩和を適用した場合。\n", "\n", "\n", " この例の処理時間は、Heron r2 プロセッサ上で約10分です。\n", "\n", "\n", "\n", "\n", "### 回路を組み立てる\n", "\n", "バックエンドを選択し、10キュービットの回路を準備してください。\n", "\n" ] }, { "cell_type": "code", "execution_count": 10, "id": "f9e93b2c-154a-4d09-872d-f770bcc669c4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\"Output" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from qiskit_ibm_runtime import QiskitRuntimeService, Executor\n", "from qiskit_ibm_runtime.quantum_program import QuantumProgram\n", "from qiskit.circuit import QuantumCircuit, Parameter\n", "from qiskit.transpiler import generate_preset_pass_manager\n", "from samplomatic.transpiler import generate_boxing_pass_manager\n", "from samplomatic import build\n", "\n", "# Initialize the service and choose a backend\n", "service = QiskitRuntimeService()\n", "backend = service.least_busy(operational=True, simulator=False)\n", "\n", "# Prepare a circuit\n", "\n", "num_qubits = 10\n", "num_layers = 10\n", "\n", "qubits = list(range(num_qubits))\n", "circuit = QuantumCircuit(num_qubits)\n", "\n", "for layer_idx in range(num_layers):\n", " circuit.rx(Parameter(f\"theta_{layer_idx}\"), qubits)\n", " for i in range(num_qubits // 2):\n", " circuit.cz(qubits[2 * i], qubits[2 * i + 1])\n", "\n", " circuit.rx(Parameter(f\"phi_{layer_idx}\"), qubits)\n", " for i in range(num_qubits // 2 - 1):\n", " circuit.cz(qubits[2 * i] + 1, qubits[2 * i + 1] + 1)\n", "\n", "circuit.draw(\"mpl\", scale=0.35, fold=100)" ] }, { "cell_type": "markdown", "id": "2d76f4f5-48b7-4123-8b6b-32eeaa06527d", "metadata": {}, "source": [ "回路とその逆回路を組み合わせると、鏡像回路が得られます。\n", "\n" ] }, { "cell_type": "code", "execution_count": 11, "id": "f8ac3f75-88ca-40f9-8382-6a427303bb8e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\"Output" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mirror_circuit = circuit.compose(circuit.inverse())\n", "mirror_circuit.measure_all()\n", "\n", "mirror_circuit.draw(\"mpl\", scale=0.35, fold=100)" ] }, { "cell_type": "markdown", "id": "2bb0692c-3d4f-4a74-807e-6b93ef4ea8d2", "metadata": {}, "source": [ "いくつかのパラメータ値を設定します：\n", "\n" ] }, { "cell_type": "code", "execution_count": 12, "id": "c1974bff-c738-43a8-8e27-b85059e2428a", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "parameter_values = np.random.rand(mirror_circuit.num_parameters)" ] }, { "cell_type": "markdown", "id": "0ca9f203-008b-4190-9e86-affefb303938", "metadata": {}, "source": [ "パスマネージャーを使用して、回路をトランスパイルし、ISA回路に変換します。\n", "\n" ] }, { "cell_type": "code", "execution_count": 13, "id": "224e0d6d-9238-4fae-aad8-f051c7e34938", "metadata": {}, "outputs": [], "source": [ "preset_pass_manager = generate_preset_pass_manager(\n", " backend=backend,\n", " optimization_level=3,\n", ")\n", "\n", "isa_circuit = preset_pass_manager.run(mirror_circuit)" ] }, { "cell_type": "markdown", "id": "9dd71644-df3d-4cf3-9e4b-9c7624b54961", "metadata": {}, "source": [ "次に、ゲートと測定値を注釈付きボックスにグループ化します。 手動で行うこともできますが、より手軽に処理したい場合は、Samplomaticの関数 `generate_boxing_pass_manager` を利用することもできます。 最初の回路には回転処理のみが適用されるため、必要なのは 注釈のみです `Twirl` 。 2番目のループは、PECの緩和策をすべて適用して実行され、および `InjectNoise` の両 `Twirl` 方の注釈が必要です。\n", "\n" ] }, { "cell_type": "code", "execution_count": 14, "id": "4afb22f1-b41f-40ed-87f7-c2d0b0f6730c", "metadata": {}, "outputs": [], "source": [ "# Pass manager used to create twirled-annotated boxes.\n", "boxing_pm = generate_boxing_pass_manager(\n", " enable_gates=True,\n", " enable_measures=True,\n", ")\n", "\n", "mirror_circuit_twirl = boxing_pm.run(isa_circuit)\n", "\n", "# Pass manager used to create a new boxed circuit with\n", "# both Twirl and InjectNoise annotations.\n", "boxing_pm = generate_boxing_pass_manager(\n", " enable_gates=True,\n", " enable_measures=True,\n", " inject_noise_targets=\"gates\", # no measurement mitigation\n", " inject_noise_strategy=\"uniform_modification\",\n", ")\n", "\n", "mirror_circuit_pec = boxing_pm.run(isa_circuit)" ] }, { "cell_type": "markdown", "id": "bd0c7cc5-48ba-47ab-84bc-41f832af3d8d", "metadata": {}, "source": [ "\n", "\n", "### ノイズについて学ぶ\n", "\n", "`InjectNoise`ノイズ学習の実験回数を最小限に抑えるため、2番目の回路（に注釈が付けられたボックスがある回路）に含まれる固有の命令を特定してください。 一意性を定義するにあたり、以下の2つの条件の両方が満たされる場合、2つのボックス命令は等しいとみなされます：\n", "\n", "* 1量子ビットゲートに至るまで、その内容は同等です。\n", "* それらの `Twirl` 注釈は等しい（それ以外の注釈はすべて無視される）。\n", "\n", "これにより、3つの独自の指示、すなわち「奇数・偶数ゲートボックス」と「最終測定ボックス」が導き出されます。\n", "\n" ] }, { "cell_type": "code", "execution_count": 15, "id": "2f8b325a-ffa4-447a-bf32-8b26b7404b0a", "metadata": {}, "outputs": [], "source": [ "from samplomatic.utils import find_unique_box_instructions\n", "\n", "unique_box_instructions = find_unique_box_instructions(\n", " mirror_circuit_pec.data\n", ")\n", "assert len(unique_box_instructions) == 3" ] }, { "cell_type": "markdown", "id": "7d496c90-fb07-49e6-a233-451ad4306102", "metadata": {}, "source": [ "`NoiseLearnerV3`を初期化し、オプションを設定して学習パラメータを選択し、ノイズ学習ジョブを実行します。\n", "\n" ] }, { "cell_type": "code", "execution_count": 16, "id": "d69204bb-4beb-4b31-b9dd-e8923889685e", "metadata": {}, "outputs": [], "source": [ "from qiskit_ibm_runtime.noise_learner_v3 import NoiseLearnerV3\n", "\n", "learner = NoiseLearnerV3(backend)\n", "\n", "learner.options.shots_per_randomization = 128\n", "learner.options.num_randomizations = 32\n", "learner.options.layer_pair_depths = [0, 1, 2, 4, 16, 32]\n", "\n", "learner_job = learner.run(unique_box_instructions)\n", "\n", "learner_job.job_id()\n", "learner_result = learner_job.result()" ] }, { "cell_type": "markdown", "id": "efe80acb-f12d-4051-84ed-d61a5a93ca23", "metadata": {}, "source": [ "メソッド `result.to_dict` を使用して、samplexが必要とするオブジェクトに変換 `result` します。\n", "\n" ] }, { "cell_type": "code", "execution_count": 17, "id": "fd63ad99-01ad-492b-9232-91f2377f97f6", "metadata": {}, "outputs": [], "source": [ "noise_maps = learner_result.to_dict(\n", " instructions=unique_box_instructions, require_refs=False\n", ")" ] }, { "cell_type": "markdown", "id": "89dd2022-db07-46f6-8737-644aa070dcdb", "metadata": {}, "source": [ "\n", "\n", "### サーキットトレーニングを行う\n", "\n", "`Executor` オブジェクトを実行します `QuantumProgram` 。 それぞれ `QuantumProgram` には複数の*項目*を含めることができ、それらはプログラムに追加されます。 各項目は、プログラムが実行すべきタスクです。\n", "\n", "空のプログラムを初期化し、各項目の各設定についてショットを要求 `1000` します。\n", "\n" ] }, { "cell_type": "code", "execution_count": 18, "id": "b48b038e-6e7d-4a6a-8f9a-df1389b644fa", "metadata": {}, "outputs": [], "source": [ "from qiskit_ibm_runtime.quantum_program import QuantumProgram\n", "\n", "# Initialize an empty QuantumProgram\n", "program = QuantumProgram(shots=1000)" ] }, { "cell_type": "markdown", "id": "845a9fdb-6837-4bda-925c-8795613bb708", "metadata": {}, "source": [ "次に、テンプレート回路とsamplexを作成し、 `mirror_circuit_twirl` それらをプログラムに追加してください。 また、samplex からランダム化データも要求 `900` してください。 つまり、このサンプレックスはパラメータのセットを生成 `900` し、各セットはQPU上で（ショット数に相当する）回実行 `1000` されます。\n", "\n", "これがこのプログラムの最初のタスクです（結果 0）。\n", "\n" ] }, { "cell_type": "code", "execution_count": 19, "id": "38942fab-f68d-44ed-b613-362b38a1ca02", "metadata": {}, "outputs": [], "source": [ "template_twirl, samplex_twirl = build(mirror_circuit_twirl)\n", "\n", "program.append_samplex_item(\n", " template_twirl,\n", " samplex=samplex_twirl,\n", " samplex_arguments={\"parameter_values\": parameter_values},\n", " shape=(900,),\n", ")" ] }, { "cell_type": "markdown", "id": "a30d1536-5339-4f19-b351-1c26891a4817", "metadata": {}, "source": [ "`mirror_circuit_pec`同様に、のために作成されたテンプレート回路とsamplexを追加し、ランダム化を要求 `900` します。 これはこのプログラムの2番目のタスクです（結果1）。\n", "\n" ] }, { "cell_type": "code", "execution_count": 20, "id": "16bbb410-b0b7-4011-b4d0-62ffbad30f41", "metadata": {}, "outputs": [], "source": [ "template_pec, samplex_pec = build(mirror_circuit_pec)\n", "\n", "program.append_samplex_item(\n", " template_pec,\n", " samplex=samplex_pec,\n", " samplex_arguments={\n", " \"parameter_values\": parameter_values,\n", " \"pauli_lindblad_maps\": noise_maps,\n", " \"noise_scales\": {\n", " ref: -1.0 for ref in noise_maps\n", " }, # Set the scales to -1 for PEC\n", " },\n", " shape=(900,),\n", ")" ] }, { "cell_type": "markdown", "id": "abf9d679-0262-4447-b889-9b06ad3cd77d", "metadata": {}, "source": [ "ジョブをインポート `Executor` して送信します。\n", "\n" ] }, { "cell_type": "code", "execution_count": 21, "id": "6231e441-87f4-480a-886a-5fdd83631e60", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Twirl result keys:\n", " ['meas', 'measurement_flips.meas']\n", "\n", "Shape of results: (900, 1000, 10)\n", "PEC result keys:\n", " ['meas', 'measurement_flips.meas', 'pauli_signs']\n", "\n", "Shape of results: (900, 1000, 10)\n" ] } ], "source": [ "from qiskit_ibm_runtime.executor import Executor\n", "\n", "executor = Executor(backend)\n", "executor_job = executor.run(program)\n", "\n", "executor_job.job_id()\n", "\n", "executor_results = executor_job.result()\n", "executor_results\n", "\n", "twirl_result = executor_results[0]\n", "\n", "print(f\"Twirl result keys:\\n {list(twirl_result.keys())}\\n\")\n", "print(f\"Shape of results: {twirl_result['meas'].shape}\")\n", "\n", "pec_result = executor_results[1]\n", "\n", "print(f\"PEC result keys:\\n {list(pec_result.keys())}\\n\")\n", "print(f\"Shape of results: {pec_result['meas'].shape}\")" ] }, { "cell_type": "markdown", "id": "021b944c-5119-4554-b4e9-2af1cfc906d8", "metadata": {}, "source": [ "\n", "\n", "### 結果を分析する\n", "\n", "`1.0`最後に、結果の後処理を行い、10個のアクティブな量子ビットのそれぞれに作用する単一量子ビットのパウリZ演算子の期待値を推定する（期待値：）。\n", "\n" ] }, { "cell_type": "code", "execution_count": 22, "id": "a24c1dd9-7f29-40b0-87ad-25c2a03e0431", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Qubit 0 -> 0.77\n", "Qubit 1 -> 0.76\n", "Qubit 2 -> 0.66\n", "Qubit 3 -> 0.71\n", "Qubit 4 -> 0.69\n", "Qubit 5 -> 0.67\n", "Qubit 6 -> 0.62\n", "Qubit 7 -> 0.59\n", "Qubit 8 -> 0.62\n", "Qubit 9 -> 0.68\n" ] } ], "source": [ "# Undo measurement twirling\n", "twirl_result_unflipped = (\n", " twirl_result[\"meas\"] ^ twirl_result[\"measurement_flips.meas\"]\n", ")\n", "\n", "# Calculate the expectation values of single-qubit Z operators\n", "exp_vals = 1 - 2 * twirl_result_unflipped.mean(axis=1).mean(axis=0)\n", "\n", "for qubit, val in enumerate(exp_vals):\n", " print(f\"Qubit {qubit} -> {np.round(val, 2)}\")" ] }, { "cell_type": "code", "execution_count": 23, "id": "8aa25d47-c4fd-4b20-a36f-fa69d7bd0971", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Qubit 0 -> 0.98\n", "Qubit 1 -> 0.99\n", "Qubit 2 -> 0.96\n", "Qubit 3 -> 0.98\n", "Qubit 4 -> 0.98\n", "Qubit 5 -> 0.98\n", "Qubit 6 -> 0.98\n", "Qubit 7 -> 0.95\n", "Qubit 8 -> 0.95\n", "Qubit 9 -> 0.94\n" ] } ], "source": [ "# Undo measurement twirling\n", "pec_result_unflipped = (\n", " pec_result[\"meas\"] ^ pec_result[\"measurement_flips.meas\"]\n", ")\n", "\n", "# Calculate the signs for PEC mitigation\n", "signs = np.prod((-1) ** pec_result[\"pauli_signs\"], axis=-1)\n", "signs = signs.reshape((signs.shape[0], 1))\n", "\n", "# Calculate the expectation values of single-qubit Z operators as required by\n", "# PEC mitigation\n", "exp_vals = 1 - (2 * pec_result_unflipped.mean(axis=1) * signs).mean(axis=0)\n", "\n", "for qubit, val in enumerate(exp_vals):\n", " print(f\"Qubit {qubit} -> {np.round(val, 2)}\")" ] }, { "cell_type": "markdown", "id": "cf25d17a-5e90-4e24-a3bf-c86f5bc3444b", "metadata": {}, "source": [ "\n", "\n", "## 次のステップ\n", "\n", "\n", " * [放送](/docs/guides/primitive-input-output#broadcasting)の概要を確認してください。\n", " * [「Executor」オプション](/docs/guides/executor-options)の使い方を学びましょう。\n", " * [指向性実行モデル](/docs/guides/directed-execution-model)を理解する。\n", " * [Samplomatic](https://qiskit.github.io/samplomatic/) のドキュメントを確認してください。\n", " * [「シェーデッド・ライトコーンを用いた確率的エラーキャンセル」](https://qiskit.github.io/qiskit-addon-slc/tutorials/01_getting_started.html) チュートリアルで、指向性実行モデルを使用する際に、さまざまなエラー軽減手法を組み合わせる方法について学びましょう。\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 }