本期文章：《物理评论A》：Online/在线发表
近日，西北大学刘维琪课题组与上海交通大学黄鹏合作，并取得一项新进展。经过不懈努力，他们通过深度学习实现了连续变量量子密钥分发系统相位自动补偿。相关研究成果已于2023年6月30日在国际知名学术期刊《物理评论A》上发表。
该研究团队提出了一种基于长短期记忆网络（LSTM）模型的CVQKD系统自动相位补偿方法。首先，通过训练LSTM模型来预测量子信号相对于局部振荡器的相位漂移随时间的变化。然后，预测的相位漂移值可用于Alice重构数据。最终，Alice和Bob可以获得原始密钥，从而提高CVQKD系统的性能和稳定性。
实验结果显示，所提出的基于LSTM的自动相位补偿算法能够准确预测相位漂移值并进行补偿，而不依赖于实时相位漂移测量，从而改善CVQKD系统的性能，同时，该方法也不需要额外的量子资源和实验硬件。
据悉，在实际的连续变量量子密钥分发（CVQKD）系统中，准确评估和补偿信号的相位漂移至关重要，以提高系统性能和稳定性。
附：英文原文
Title: Automatic phase compensation of a continuous-variable quantum-key-distribution system via deep learning
Author: Zhe-Kun Zhang, Wei-Qi Liu, Jin Qi, Chen He, Peng Huang
Issue&Volume: 2023/06/30
Abstract: In a practical continuous-variable quantum-key-distribution (CVQKD) system, it is vital to accurately evaluate and then compensate for the phase drifts of the signals, so that the involved system can achieve better performance and stability. In this paper, based on the long short-term memory network (LSTM) model, an automatic phase compensation approach of the CVQKD system is proposed. The LSTM model is first trained to predict the phase drift value of the quantum signal relative to the local oscillator over time. Then, the predicted phase drift value can be used by Alice to reconstruct her data. Finally, Alice and Bob can obtain the raw key, so that the CVQKD system can achieve enhancements in terms of performance and stability. The experimental results indicate that the proposed LSTM-based automatic phase compensation algorithm can accurately predict the phase drift value and perform phase compensation instead of real-time phase drift measurement, which improves the performance of the CVQKD system without requiring any additional quantum resources and extra experimental hardware.
DOI: 10.1103/PhysRevA.107.062614
Source: https://journals.aps.org/pra/abstract/10.1103/PhysRevA.107.062614
期刊信息
Physical Review A：《物理评论A》，创刊于1970年。隶属于美国物理学会，最新IF：2.97
官方网址：https://journals.aps.org/pra/
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