基于BP 神经网络的经典-量子信号共纤同传系统参数预测

doi:10.3969/j.issn.1007-5461.2023.04.014

摘要/Abstract

摘要： 光纤量子密钥分发的应用推广取决于与现有光网络的兼容性, 而利用波分复用技术将经典数据和量子信号进行共纤传输兼备安全性、经济性和实用性等优势。针对经典 - 量子信号共纤同传系统中信号态平均光子数、诱骗态种类数量等参数最优取值处理困难、运行速度缓慢等影响其实用化的突出问题, 建模分析了主要噪声成分, 并在考虑统计波动影响下对有限长效应和诱骗态方法进行了评估。进而利用原始信号数据集对反向传播 (BP) 神经网络进行训练, 以实现不同信道噪声条件下的信号态平均光子数等系统参数的预测。结果表明, 该网络输出的预测平均光子数取值与原始曲线取值结果基本一致, 训练误差小于10−3。该网络可作为一种有效模型用于实用化诱骗态经典- 量子共纤同传系统参数预测, 对量子保密通信向着高速率、大容量、智能化发展具有潜在的应用价值。

关键词: 量子光学, 量子密钥分发, 波分复用, 机器学习, 神经网络

Abstract: The application of fiber-based quantum key distribution (QKD) depends on the compatibility with existing optical networks. The use of wavelength division multiplexing (WDM) technology for cofiber transmission of classical data and quantum signals has the advantages of security, economy, and practicality. Aiming at the prominent problems that affect the application of the classical-quantum signal co-fiber transmission system, such as the difficulty in optimizing and calculating the average photon number of signal states, the number of decoy states and other parameters, and the slow running speed, the channel noise components are modelel and analyzed, and the finite-length effect and the decoy state method are evaluated considering the statistical fluctuations. Furthermore, based on the experimental data set, the back propagation (BP) neural network is trained to predict the system parameters such as the average photon number of signal states under different channel noise conditions. The results show that the predicted average photon number by the BP network is basically consistent with the original curve, and the training error is less than 10−3. It is indicated that the network can be used as an effective model for practical prediction of the parameters of the decoy-state classical-quantum co-fiber transmission system, which is of great practical significance for the development of quantum-secure communication towards high speed, large capacity and intelligence.

Key words: quantum optics, quantum key distribution, wavelength division multiplexed, machine learning, neural network

中图分类号:

TN918

孙一石, 孙弋 . 基于BP 神经网络的经典-量子信号共纤同传系统参数预测[J]. 量子电子学报, 2023, 40(4): 546-559.

SUN Yishi , SUN Yi . Parameter prediction of classical-quantum signals co-fiber transmission system based on BP neural network[J]. Chinese Journal of Quantum Electronics, 2023, 40(4): 546-559.

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