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エンジニア入門シリーズ

Pythonではじめる量子AI入門
量子機械学習から量子回路自動設計まで

著: 曽我部 東馬 (電気通信大学)
定価: 3,960円(本体3,600円+税)
判型: B5変型
ページ数: 216 ページ
ISBN: 978-4-910558-32-5
発売日: 2024/8/22
管理No: 130

【目次】

第1章 量子コンピューティングの基礎

  1. 1.1 量子コンピュータの歴史
  2. 1.2 量子コンピュータの種類と開発状況
  3. 1.3 量子コンピューティングの基本要素
    1. 1.3.1 量子回路要素: 量子ビットの表記
    2. 1.3.2 量子ビットの基本演算
    3. 1.3.3 量子回路要素:量子ゲート
    4. 1.3.4 量子回路要素:2量子ビット以上量子ゲート
    5. 1.3.5 量子回路要素:量子測定
    6. 1.3.6 Pythonによる量子回路の作成
    7. 1.3.7 Pythonを用いた1量子ビット量子回路コンピューティング
    8. 1.3.8 2量子ビット以上のPython量子コンピューティング
  4. 1.4 量子アルゴリズム
    1. 1.4.1 量子加算アルゴリズム
    2. 1.4.2 量子もつれと量子テレポーテーション
    3. 1.4.3 量子もつれとEPRパラドックス (ベルの不等式、CHSHの不等式)
    4. 1.4.4 量子アルゴリズムの鍵:位相キックバック
    5. 1.4.5 量子フーリエ変換アルゴリズムの実装
    6. 1.4.6 量子位相推定アルゴリズムの実装
    7. 1.4.7 Deutsch-Jozsa量子アルゴリズムの実装
    8. 1.4.8 グローバーのアルゴリズムの実装
  5. まとめ
  6. 参考文献

第2章 機械学習と量子機械学習の導入

  1. 2.1 機械学習の基本法則:バイアスとバリアンス
  2. 2.2 教師あり学習
    1. 2.2.1 回帰と分類
    2. 2.2.2 学習モデルと代表的なアルゴリズム
  3. 2.3 教師なし学習-特徴抽出・クラスタリング・次元削減
    1. 2.3.1 次元削減とクラスタリングの等価性
    2. 2.3.2 行列方式による次元削減手法:主成分分析
    3. 2.3.3 競合学習クラスタリングによる次元削減
  4. 2.4 量子機械学習
  5. 2.5 NISQ時代における量子機械学習
  6. まとめ
  7. 参考文献

第3章 量子機械学習アルゴリズムⅠ

  1. 3.1 情報エンコーディング
    1. 3.1.1 基底エンコーディング
    2. 3.1.2 振幅エンコーディング
    3. 3.1.3 テンソル積エンコーディング
  2. 3.2 量子特徴マッピング
    1. 3.2.1 量子カーネルの導入
    2. 3.2.2 SWAPテストを用いた量子カーネル回路
    3. 3.2.3 データエンコード回路を利用した量子カーネル回路
  3. 3.3 Harrow-Hassidim-Lloyd (HHL) アルゴリズム
  4. 3.4 量子状態ベクトル距離計算
  5. 3.5 ハイブリッド型量子k-meansクラスタリング手法
  6. 3.6 量子カーネルSVM法
  7. 3.7 量子回路学習アルゴリズムの実装と応用例
  8. まとめ
  9. 参考文献

第4章 量子機械学習アルゴリズムⅡ

  1. 4.1 変分量子固有値ソルバー (VQE) の実装と応用例
  2. 4.2 量子近似最適化アルゴリズム (QAOA) の実装と応用例
  3. 4.3 AI駆動型量子回路自動設計
    1. 4.3.1 量子回路設計のQOMDP手法の概要
    2. 4.3.2 GHZ状態生成
  4. まとめ
  5. 参考文献

付録

  1. A 量子回路課題の解答
  2. B Google ColabでのQiskitのインストール方法および実行手順
  3. C 式(1.65)の証明
  4. D 式(1.74)の証明
  5. E 有限差分法
  6. F 同時摂動最適化法 (SPSA)
  7. G 量子部分観測マルコフ決定過程手法 (QOMDP)
    1. G.1 クラウス行列
    2. G.2 QOMDP
    3. G.3 QOMDPにおけるプランニングアルゴリズム
      1. G.3.1 価値関数
      2. G.3.2 プランニングアルゴリズム
      3. G.3.3 方策
  8. 参考文献

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