高速量子ランダム数生成のためのディープラーニングベースのミニエントロピー加速評価
Xiaomin Guo1,2, Wenhe Zhou1,2, Yue Luo1,2
1Key Laboratory of Advanced Transducers and Intelligent Control System, Ministry of Education, Taiyuan University of Technology, Taiyuan 030024, China.
Entropy (Basel, Switzerland)
|August 28, 2025
まとめ
この研究は,極化制御ヘテロジン検出を使用して量子ランダムナンバー生成 (QRNG) を強化します. 高速で安全なランダムビット生成と急速なエントロピー評価を実現し,QRNGの実用的なアプリケーションを改善します.
科学分野:
- 量子情報科学
- 安全な通信技術
- 応用物理学
背景:
- 安全な通信は,高速で安全な量子ランダムナンバー生成 (QRNG) に依存しています.
- QRNGシステムは,効率とセキュリティに影響を与える非理想的な問題に直面しています.
- 正確なエントロピーの評価はランダム性を定量化するために重要です.
研究 の 目的:
- QRNGの効率とセキュリティを向上させる
- 量子ランダム性に対するシステムの非理想性の影響を調査する.
- QRNGでエントロピーを評価するための迅速で正確な方法を開発する.
主な方法:
- 真空射撃騒音の変動を測定するために,極化制御ヘテロジン検出を使用した.
- 不均衡の検出,振幅相の重なり, 量子条件の最小エントロピーの安全性パラメータを分析した.
- 急速なエントロピーの評価のための深層収束神経ネットワーク (CNN) を開発した.
主要な成果:
- 高セキュリティパラメータで37.25Gbpsで83.16%の真のランダムビット抽出比率を達成しました.
- ランダム性の過大評価の緩和と 盗聴に対するセキュリティの強化
- CNNは,広範囲にわたる二乗データを高速で処理しました (MAPEは0.004です).
- デュアルクアドレートヘテロダインの検出は85 Gbpsの生成速度を超えました.
結論:
- 提案された方法は,QRNGの性能とセキュリティを大幅に改善します.
- CNNを用いた急速なエントロピーの評価は,QRNGの実用的な展開を加速します.
- この研究は高速で安全なランダムナンバー生成の 発展を進めています
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