Parameter estimation of continuous variable quantum key distribution system via artificial neural networks

Continuous-variable quantum key distribution(CVQKD)allows legitimate parties to extract and exchange secret keys.However,the tradeoff between the secret key rate and the accuracy of parameter estimation still around the present CVQKD system.In this paper,we suggest an approach for parameter estimation of the CVQKD system via artificial neural networks(ANN),which can be merged in post-processing with less additional devices.The ANN-based training scheme,enables key prediction without exposing any raw key.Experimental results show that the error between the predicted values and the true ones is in a reasonable range.The CVQKD system can be improved in terms of the secret key rate and the parameter estimation,which involves less additional devices than the traditional CVQKD system.     

基于峰值补偿的连续变量量子密钥分发方案

在实际的连续变量量子密钥分发系统中,接收端模数转换器的有限采样带宽会导致脉冲峰值采样结果不准确,从而使参数估计过程产生误差,给窃听者留下了安全性漏洞.针对这个问题,本文提出一种基于峰值补偿的连续变量量子密钥分发方案,利用高斯脉冲的基本特性来估计每个脉冲的最大采样值与脉冲峰值之间的偏差,从而对该采样值进行峰值补偿,使系统...