基于深度学习的微加速评估用于高速量子随机数生成
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
概括
这项研究通过极化控制的异质因子检测增强了量子随机数生成 (QRNG). 它实现了高速,安全的随机位生成和快速评估,改进了实际的QRNG应用.
科学领域:
- 量子信息科学
- 安全的通信技术
- 应用物理
背景情况:
- 安全的通信依赖于高速,高安全的量子随机数生成 (QRNG).
- 实际的QRNG系统面临影响效率和安全性的非理想性.
- 准确的值对于量化随机性至关重要.
研究的目的:
- 通过新的响应方式提高QRNG的效率和安全性.
- 研究系统非理想性对量子随机性的影响.
- 开发一个快速而准确的方法来评估QRNG中的.
主要方法:
- 采用极化控制的异质体检测来测量真空射击噪声波动.
- 分析了不平衡检测,振幅相重叠,以及量子条件最小的安全参数.
- 开发了一个深度卷积神经网络 (CNN) 以快速评估.
主要成果:
- 在37.25Gbps的高安全参数中实现了83.16%的真实随机比特提取比率.
- 已证实可以缓解随机性过度估计和加强对窃听的安全性.
- 美国国家广播公司以高准确度快速处理了大量的平方数据 (MAPE为0.004).
- 超出了85Gbps的生成速度.
结论:
- 拟议的方法显著提高了QRNG的性能和安全性.
- 使用CNN的快速值加速了QRNG的实际部署.
- 这项工作促进了高速,安全的随机数生成的发展.
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