Although the continuous-variable quantum key distribution (CV-QKD) protocol based on a local local oscillator (LLO) can close all the security loopholes from the transmitted local oscillator (TLO), the phase noise caused by the inaccurate phase reference information limits the performance of the protocol. To reduce the residual phase noise, in this work, we propose a phase estimation and compensation method based on the temporal convolutional neural (TCN) model, where a part of phase information obtained by measuring pilot pulses is employed as the training data and input into the TCN module. With a trained TCN module, the subsequent phase drifts can be more accurately estimated, allowing for better phase compensation and lower phase noise. Numerical analysis shows that the proposed scheme can improve the transmission distance and the secret key rate of the LLO protocol.

中文翻译：

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通过时间卷积神经网络进行连续可变量子密钥分布的相位补偿

虽然基于本地本地振荡器（LLO）的连续可变量子密钥分发（CV-QKD）协议可以堵住传输本地振荡器（TLO）的所有安全漏洞，但不准确的相位参考信息引起的相位噪声限制了该协议的安全性。协议的性能。为了减少残余相位噪声，在这项工作中，我们提出了一种基于时间卷积神经（TCN）模型的相位估计和补偿方法，其中通过测量导频脉冲获得的部分相位信息被用作训练数据并输入到TCN 模块。通过经过训练的 TCN 模块，可以更准确地估计后续相位漂移，从而实现更好的相位补偿和更低的相位噪声。数值分析表明，该方案能够提高LLO协议的传输距离和密钥率。