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被动连续变量测量设备独立的量子密钥分布可用机器学习预测在海洋流中
Jianmin Yi1, Hao Wu1, Ying Guo1,2
1School of Automation, Central South University, Changsha 410083, China.
Entropy (Basel, Switzerland)
|March 28, 2024
概括
本研究提出了一种机器学习方法,用于预测连续变量 (CV) 量子密钥分布 (QKD) 的海洋量子通信通道特征. 这一进步提高了安全的水下量子网络的可行性,用于各种应用.
科学领域:
- 量子信息科学 量子信息科学
- 海洋工程是海洋工程.
- 机器学习应用 机器学习应用
背景情况:
- 水下量子网络对于海洋探索,环境监测和国防至关重要.
- 海洋动荡对可靠的量子通信道构成重大挑战.
研究的目的:
- 开发一种机器学习方法,用于预测水下连续变量 (CV) 量子密钥分布 (QKD) 中的通道特征.
- 评估被动CV-MDI-QKD在具有挑战性的海水环境中的可行性.
主要方法:
- 利用神经网络来预测海洋量子链接的传输率.
- 专注于被动CV-MDI-QKD,利用更简单的线性元素来最大限度地减少环境相互作用.
主要成果:
- 机器学习模型与真实世界海洋数据显示出良好的一致性.
- 预测在可接受的错误范围内,验证了该方法.
结论:
- 拟议的机器学习方法对表征水下量子通信通道有希望.
- 被动CV-QKD是商业化和在海洋环境中实施的更可行的选择.
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