基于神经网络的预测,通过海水通道通过水下连续变量量子密钥分布的秘密密钥速率
Yun Mao1,2, Yiwu Zhu2, Hui Hu2
1School of Information Engineering, Shaoyang University, Shaoyang 422000, China.
Entropy (Basel, Switzerland)
|June 28, 2023
概括
本研究介绍了一种神经网络方法,用于预测在水下通道上的连续变量量子密钥分布 (CVQKD) 中的秘密密钥率. 与传统方法相比,基于LSTM的神经网络显著提高了性能.
科学领域:
- 量子通信 量子通信是一种量子通信.
- 应用物理 应用物理
- 机器学习 机器学习
背景情况:
- 连续变量量子密钥分布 (CVQKD) 对于安全通信至关重要,因为它具有成本效益的光学实现.
- 水下通道给量子通信系统带来了独特的挑战.
- 预测秘密密钥速率对于评估CVQKD的性能和安全性至关重要.
研究的目的:
- 研究一种神经网络方法,通过水下通道预测离散调制CVQKD的秘密密钥速率.
- 评估基于长期短期记忆 (LSTM) 的神经网络对此任务的性能.
- 为了证明改善实际量子通信系统的潜力.
主要方法:
- 利用基于长短期记忆 (LSTM) 的神经网络 (NN) 模型.
- 模拟的连续变量量子密钥分布 (CVQKD) 通过水下通道进行离散调制 (DM).
- 将基于LSTM的NN性能与基于向后传播 (BP) 的NN进行了比较,用于秘密密钥率预测.
主要成果:
- 基于LSTM的神经网络在有限大小分析中实现了秘密密钥率的下限.
- 基于LSTM的NN表现明显优于基于BP的NN.
- 这种方法使得可以快速推导出水下CVQKD的秘密密钥率.
结论:
- 基于LSTM的神经网络是预测水下环境中CVQKD的秘密关键速率的高效方法.
- 这种方法比传统的神经网络模型提供了显著的性能改善.
- 这些发现支持这种方法用于增强实际量子通信系统的应用.
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