一种基于机器学习的量子随机数预测方法
Leveraging Machine Learning for Quantum Random Number Prediction
DOI: 10.12677/airr.2026.152052, PDF,   
作者: 韩 宇*, 费洋扬：河南省网络密码技术重点实验室，河南 郑州
关键词: 量子随机数；机器学习；随机数预测方法；Quantum Random Numbers； Machine Learning； Random Number Prediction Method
摘要: 随机性是密码学等领域的关键资源，量子随机数被视为真随机性生成的重要标准，但现有NIST统计测试难以全面评估其不可预测性与脆弱性。本文提出一种基于机器学习的量子随机数预测方法，以激光相位噪声方案的量子随机数为研究对象，通过构建数据集、选取适配的全连接网络模型，利用历史随机序列预测未来输出，以预测概率量化其熵值上限与脆弱性关键因素。该方法可补充传统统计测试，为随机数安全性分析提供新工具，适用于密码学场景适配与设备优化。
Abstract: Randomness is a vital resource in fields such as cryptography, and quantum random numbers are widely recognized as the standard for generating true randomness. However, existing NIST statistical tests fail to fully assess their unpredictability and vulnerability. This article proposes a QRN prediction method based on machine learning, focusing on QRNs generated via the laser phase noise scheme. By constructing a dedicated dataset, selecting a suitable fully connected network model, and leveraging historical random sequences to predict future outputs, the upper bound of entropy and key vulnerability factors are quantified by means of prediction probability. This method serves as a complement to traditional statistical tests, offers a novel tool for random number security analysis, and is well-suited for cryptographic scenario adaptation and device optimization.
文章引用：韩宇, 费洋扬. 一种基于机器学习的量子随机数预测方法[J]. 人工智能与机器人研究, 2026, 15(2): 538-547. https://doi.org/10.12677/airr.2026.152052

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