content/lab_3/ja/lab-3-ai-transpiler-ja.ipynb

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   ]
  },
  {
   "cell_type": "markdown",
   "id": "f67ec71c-206b-45fc-a6f2-ec58b4891d4c",
   "metadata": {},
   "source": [
    "# Lab 3: What's Next - AIを活用した Qiskit Transpiler Service を使ってみる"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8979f3ca-d980-49c5-bda4-d7f59ed033f6",
   "metadata": {},
   "source": [
    "# Table of Contents\n",
    "\n",
    "* [Introduction to AI-Powered Transpiler Passes](Introduction-to-AI-Powered-Transpiler-Passes)\n",
    "    * [Exercise 1](#exercise1)\n",
    "    * [Exercise 2](#exercise2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf00347e-9ffd-4f4d-9270-21eb512f8f96",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c4bb49c-26f3-4b0a-b8fa-e25f2882a688",
   "metadata": {
    "id": "5a76cfa8-9f5d-4716-a007-c13bcb54b82b"
   },
   "source": [
    "いつものように、オプションのインストールから始めます。QiskitのインストールとAPIトークンのセルは、以前の Lab で保存していない場合のみ実行する必要があります。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abb25eb9-4327-4ae0-9db4-745b87194a3d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "### Install Qiskit and relevant packages, if needed\n",
    "### IMPORTANT: Make sure you are using python 3.10 or 3.11 for compatibility of the required packages\n",
    "%pip install qiskit[visualization]==1.0.2\n",
    "%pip install qiskit-ibm-runtime\n",
    "%pip install qiskit-aer\n",
    "%pip install graphviz\n",
    "%pip install qiskit-transpiler-service\n",
    "%pip install git+https://github.com/qiskit-community/Quantum-Challenge-Grader.git -U"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef1f5086",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Save API Token, if needed\n",
    "\n",
    "%set_env QXToken=deleteThisAndPasteYourTokenHere\n",
    "\n",
    "# Make sure there is no space between the equal sign\n",
    "# and the beginning of your token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d9fbcbc-8f64-4056-afcb-bcf700caa779",
   "metadata": {},
   "outputs": [],
   "source": [
    "# qc-grader should be 0.18.10 (or higher)\n",
    "import qc_grader\n",
    "\n",
    "qc_grader.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1aff5103-c92a-45b0-93bb-f5b034de45c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from qiskit.circuit.library import EfficientSU2\n",
    "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n",
    "from qiskit_ibm_runtime import QiskitRuntimeService\n",
    "from qiskit_transpiler_service.transpiler_service import TranspilerService"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08927292-6f1a-4490-901a-fdcdc3b411f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import for grader\n",
    "from qc_grader.challenges.iqc_2024 import grade_lab3_ait_ex1, grade_lab3_ait_ex2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "545418e9-a530-4e11-a323-d01177dd1da0",
   "metadata": {},
   "source": [
    "## はじめに\n",
    "\n",
    "Lab 3 へようこそ！ 本日は、Qiskit スタック用に開発されているエキサイティングな新機能の一部を、特別にご紹介します。この教育的な Challenge は notebook が分かれていて、それぞれがユニークで画期的なイノベーションを紹介し、これらの新機能を探索しテストを促します。複雑な量子回路を簡素化する Circuit Knitting Toolboxのようなツールを探求し、Qiskit Serverless で量子コンピューティングと古典コンピューティングのシームレスな統合をセットアップするワークフローを体験し、AI-Powered Transpiler Passes と コード生成と回路最適化のための Qiskit Code Assistant のような AI 搭載ツールを活用するチャンスがあります。これらのイノベーションが量子開発にどのような革命をもたらすのか、ぜひご覧ください！\n",
    "\n",
    "<div class=\"alert alert-block alert-danger\">\n",
    "    \n",
    "これらの機能は **ベータ版およびリリースプレビュー版** であるため、バグに遭遇する可能性が高いことにご注意ください。量子開発の限界に挑戦してください！\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "430cc7b6-46f2-4077-9746-edbb079ff0db",
   "metadata": {},
   "source": [
    "# Qiskit Transpiler service を使った AI 搭載 transpiler passes の紹介\n",
    "\n",
    "\n",
    "このハンズオン演習では、Qiskit の AI 搭載 transpiler passes の最先端機能を探求する機会を提供します。Qiskit Transpiler Service の最近のベータ版リリースにより、 AI と IBM Cloud&reg; リソースを活用して量子回路をより効率的に最適化できるようになりました。\n",
    "\n",
    "特定の量子デバイスの制約や特性に合わせて抽象的な量子回路をトランスパイルすることは、量子コンピューティングのワークフローにおいて非常に重要なステップです。Lab 2 で学んだように、このプロセスは従来、ヒューリスティックなアルゴリズムに依存していましたが、新しいAIを搭載したtranspiler passes は、実用的なアプリケーションに十分な速度でありながら、より効率的で高品質なトランスパイルの可能性を提供します。今回のチャレンジでは、Qiskit Transpiler Service のベータ版へのアクセス権を提供し、実験していただきます。\n",
    "\n",
    "**このツールの詳細については、 [ブログ](https://www.ibm.com/quantum/blog/ai-transpiler-passes) と [arxiv論文](https://arxiv.org/abs/2405.13196) をご覧ください。** ツールのドキュメントは [IBM Quantum Platform docs](https://docs.quantum.ibm.com/transpile/qiskit-transpiler-service) にあります。\n",
    "\n",
    "この Exercise では、このベータ・リリース・プレビューで、 AI 搭載 transpiler passes を実験し、従来の手法と比較し、量子コンピューティング・ワークフローをどのように強化できるかを理解します。さあ、量子回路最適化の未来に飛び込んでみましょう！"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75b7f2b4-31e5-4ebc-82fd-330f0bcdd807",
   "metadata": {},
   "source": [
    "## Step 1: 回路の作成\n",
    "\n",
    "Qiskit の circuit library を使って量子回路を作成します。このステップでは、量子回路の構造とゲートを定義します。これは、その後のトランスパイルと最適化のタスクの基礎となります。ここでは、元の [論文](https://arxiv.org/abs/2405.13196) で示された回路と非常によく似た回路を使用します。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da050482-4412-41e2-9548-9e84a45749c3",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "\n",
    "**重要:** `qiskit-transpiler-service` パッケージがインストールされ、IBM Quantum Platform を使用するようにトークンが設定されていることを確認してください。この方法については、 [こちら](https://docs.quantum.ibm.com/start/setup-channel#set-up-to-use-ibm-quantum-platfor) のドキュメントリンクを参照してください。\n",
    "\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "392639ca-7190-4c32-b531-b41960e7ea91",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "NUM_QUBITS = 61"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8502685d-24fd-4a0b-b993-45b185b72a07",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "circuit = EfficientSU2(NUM_QUBITS, entanglement=\"circular\", reps=1).decompose()\n",
    "\n",
    "print(f\"Original circuit -> Depth: {circuit.depth()}, CNOTs: {circuit.num_nonlocal_gates()}\")\n",
    "circuit.draw(fold=-1, output=\"mpl\", style=\"iqp\", scale=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46eec6f3-d4d5-40fd-97fa-d7fca0fa8344",
   "metadata": {},
   "source": [
    "## Step 2: 回路のトランスパイルと最適化\n",
    "\n",
    "このステップでは、Qiskit Transpiler Service を使って回路をトランスパイルし、最適化します。このプロセスを2回実行します。最初はAIなしで、次にAIを使用した transpiler passes を使用します。このセクションでは Qiskit Transpiler Service を使用します。Qiskit Transpiler Service は、クラウド上でヒューリスティックなトランスパイラー機能とAIによるトランスパイラー機能の両方を提供します。IBM Quantum™ のクラウドリソースと高度な AI 搭載 transpiler passes の恩恵を受けるために、実行中のトランスパイル・タスクを活用することができます。 **詳細については、 [こちら](https://docs.quantum.ibm.com/transpile/qiskit-transpiler-service) の Qiskit Transpiler Serviceのドキュメントを参照してください。** \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd951713-03e5-4f81-8ff3-c3e60c3d3772",
   "metadata": {},
   "source": [
    "#### Qiskit Transpiler Service - AI なし\n",
    "\n",
    "AIなし：Qiskit Transpiler Service で利用可能な従来のヒューリスティック手法を使って回路をトランスパイルします。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c665e734-ebb8-4f66-95d9-6e752d9c7370",
   "metadata": {},
   "source": [
    "<a id=\"exercise1\"></a>\n",
    "<div class=\"alert alert-block alert-success\">\n",
    "    \n",
    "<b> Exercise 1:</b> \n",
    "\n",
    "Qiskit Transpiler Service をセットアップして、クラウド上に回路をトランスパイルしましょう。Qiskit Transpiler Service の最新版がインストールされ、設定されていることを確認してください。この演習では `ibm_brisbane` バックエンドを使用します。バックエンドを設定し、 **AI フラグをオンにせず、** optimization level  `3` で transpiler を構成します。\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c00dae26-e073-45d0-ba88-4d24db222cc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "transpiler_ai_false = TranspilerService( \n",
    "    # Add your code here\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47949823-1053-43d7-8139-57784c763034",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Submit your answer using following code\n",
    "\n",
    "grade_lab3_ait_ex1(transpiler_ai_false) # Expected result type: TranspilerService"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b63cdca-a872-406f-bab0-08965eaacbd0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "circuit_ai_false = transpiler_ai_false.run(circuit)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3350447e-1e1e-4488-a745-2ef0bdba0183",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "\n",
    "エラー `FileNotFoundError: [Errno 2] No such file or directory: ‘/root/.qiskit/qiskit-ibm.json’` または `TranspilerError: 'Service error: 403 Client Error: Forbidden for url` が発生した場合は、以下を試してください。\n",
    "\n",
    "<pre><code>\n",
    "QiskitRuntimeService.save_account(\n",
    "    channel=\"ibm_quantum\",\n",
    "    token=\"<MY_IBM_QUANTUM_TOKEN>\",\n",
    "    set_as_default=True,\n",
    "    # Use `overwrite=True` if you're updating your token.\n",
    "    overwrite=True,\n",
    ")\n",
    " \n",
    "# Load saved credentials\n",
    "service = QiskitRuntimeService()\n",
    "\n",
    "</code></pre>\n",
    "\n",
    "IBM Quantum トークンの設定方法の詳細については、 [このドキュメント ページ](https://docs.quantum.ibm.com/start/setup-channel#set-up-to-use-ibm-quantum-platform) を確認してください。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "358aa7f2-3b15-47eb-a869-185751aa44de",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "print(f\"Transpiled without AI -> Depth: {circuit_ai_false.depth()}, CNOTs: {circuit_ai_false.num_nonlocal_gates()}\")\n",
    "circuit_ai_false.draw(fold=-1, output=\"mpl\", scale=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fda47e18-4953-4d48-a726-260925dffa97",
   "metadata": {},
   "source": [
    "#### Qiskit Transpiler Service - AI搭載\n",
    "AIを搭載した transpiler passes を有効にして、回路の最適化をどのように改善できる可能性があるかを確認します。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41e2ff99-2cd5-4079-8c0c-9fd973f29d99",
   "metadata": {},
   "source": [
    "<a id=\"exercise2\"></a>\n",
    "<div class=\"alert alert-block alert-success\">\n",
    "    \n",
    "<b> Exercise 2:</b> \n",
    "\n",
    "それでは AI フラグを `on` にして Qiskit Transpiler サービスを使用してみましょう。環境が Qiskit Transpiler Service の最新バージョンでセットアップされていることを確認してください。引き続き `ibm_brisbane` バックエンドを使用します。トランスパイラーを optimization level `3` で設定し、AI フラグを有効にして. AI による高度な最適化を活用します。このセットアップでは、AI を活用してゲート数と回路深度を最適化することで、量子回路のトランスパイルを試みます。\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6cedf51d-4391-4ef7-8c8a-8f14fdb72e45",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "transpiler_ai_true = TranspilerService(\n",
    "    # Add your code here\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a9b1623-b617-415d-b112-6d209eae4574",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Submit your answer using following code\n",
    "\n",
    "grade_lab3_ait_ex2(transpiler_ai_true) # Expected result type: TranspilerService"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c14843e-e056-4580-820e-1dbd75ea3152",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "circuit_ai_true = transpiler_ai_true.run(circuit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e04470d2-d901-4757-bbdc-bce4703b1406",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "print(f\"Transpiled with AI -> Depth: {circuit_ai_true.depth()}, CNOTs: {circuit_ai_true.num_nonlocal_gates()}\")\n",
    "circuit_ai_true.draw(fold=-1, output=\"mpl\", scale=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91e2aa6f-fa83-4496-b709-b5a9b9893b49",
   "metadata": {},
   "source": [
    "## Step 3: グラフのプロット\n",
    "トランスパイルと最適化の結果を可視化します。グラフをプロットして、 AI を使用してトランスパイルした回路と AI を使用しない回路の性能指標（深さ、ゲート数など）を比較します。これにより、 AI 搭載 transpiler passes が量子回路に与える影響を理解することができます。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03164f5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transpiling locally using Qiskit SDK\n",
    "service = QiskitRuntimeService()\n",
    "backend = service.backend(\"ibm_sherbrooke\")\n",
    "pm = generate_preset_pass_manager(backend=backend, optimization_level=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42d031ed-ba31-4b6e-8bc4-bdb2257898fd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Run and compile results\n",
    "num_qubits = [11, 21, 41, 61, 81]\n",
    "num_cnots_local = []\n",
    "num_cnots_with_ai = []\n",
    "num_cnots_without_ai = []\n",
    "\n",
    "for nq in num_qubits:\n",
    "    circuit = EfficientSU2(nq, entanglement=\"circular\", reps=1).decompose()\n",
    "\n",
    "    # Using the Transpiler locally on Qiskit \n",
    "    circuit_local = pm.run(circuit)\n",
    "    # Using the transpiler service without AI\n",
    "    circuit_without_ai = transpiler_ai_false.run(circuit)\n",
    "    # Using the transpiler service with AI\n",
    "    circuit_with_ai = transpiler_ai_true.run(circuit)\n",
    "\n",
    "    num_cnots_local.append(circuit_local.num_nonlocal_gates())\n",
    "    num_cnots_without_ai.append(circuit_without_ai.num_nonlocal_gates())\n",
    "    num_cnots_with_ai.append(circuit_with_ai.num_nonlocal_gates())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "20505da5-f19d-490b-a3ae-2d00450b2a07",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x16895a0d0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(num_qubits, num_cnots_with_ai, '.-')\n",
    "plt.plot(num_qubits, num_cnots_without_ai, '.-')\n",
    "plt.plot(num_qubits, num_cnots_local, '--')\n",
    "plt.xlabel(\"Number of qubits\")\n",
    "plt.ylabel(\"CNOT count\")\n",
    "plt.legend([\"Qiskit Transpiler Service with AI\", \"Qiskit Transpiler Service without AI\", \"Qiskit SDK\"])"
   ]
  },
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    "<a id=\"exercise5\"></a>\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "    \n",
    "<b> ボーナス Exercise! </b> \n",
    "\n",
    "AIトランスパイラーで何ができるかを体験したところで、 Qiskit Transpiler Service を使用して、 様々な高い深さ (Depth) および高い幅 (Width) の回路を試してみてください！\n",
    "\n",
    "また、Lab 2 で学んだ「従来の」Qiskit passes のドロップイン置き換えとして機能する AI 搭載 transpiler passes を、いくつかの transpiling タスクで確認することもできます。これについては [こちら](https://docs.quantum.ibm.com/transpile/ai-transpiler-passes) をご覧ください。\n",
    "\n",
    "結果を投稿し、ゲートの深さと CNOT 数の最もよく改善されたものを Discord で披露してください！\n",
    "\n",
    "あなたの発見を自由に紹介し、コミュニティと成果を議論してください！\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b05aaa22-2e3b-433d-81cd-a583ba5598c4",
   "metadata": {},
   "source": [
    "AI Transpiler セクションの完了おめでとうございます！Qiskitの AI 搭載 transpiler passes の最先端機能を探求する機会を得ました。この革新的な機能は、 AI を活用して量子回路を最適化し、より効率的で効果的な実行を提供します。 AI を活用した最適化の実験を続けることで、量子コンピューティングの限界をさらに押し広げることができます。スキルを磨き、量子開発における AI の可能性を探求し続けてください。これからも素晴らしい仕事を続けてください！"
   ]
  },
  {
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   "id": "2dfa65c3-755d-4272-924f-7154c162b054",
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   "source": [
    "# 追加情報\n",
    "\n",
    "**Created by:** Vishal Sharathchandra Bajpe, David Kremer\n",
    "\n",
    "**Advised by:** Junye Huang, Juan Cruz Benito\n",
    "\n",
    "**Translated by:** Kifumi Numata\n",
    "\n",
    "**Version:** 1.4.2"
   ]
  }
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