Analysis of atmospheric effects on the continuous variable quantum key distribution

Atmospheric effects have significant influence on the performance of a free-space optical continuous variable quantum key distribution(CVQKD)system.In this paper,we investigate how the transmittance,excess noise and interruption probability caused by atmospheric effects affect the secret-key rate(SKR)of the CVQKD.Three signal wavelengths,two weather conditions,two detection schemes,and two types of attacks are considered in our investigation.An expression aims at calculating the interruption probability is proposed based on the Kolmogorov spectrum model.The results show that a signal using long working wavelength can propagate much further than that of using short wavelength.Moreover,as the wavelength increases,the influence of interruption probability on the SKR becomes more significant,especially within a certain transmission distance.Therefore,interruption probability must be considered for CVQKD by using long-signal wavelengths.Furthermore,different detection schemes used by the receiver will result in different transmission distances when subjected to individual attacks and collective attacks,respectively.

Multiparameter performance monitoring of pulse amplitude modulation channels using convolutional neural networks

A designed visual geometry group(VGG)-based convolutional neural network(CNN)model with small computational cost and high accuracy is utilized to monitor pulse amplitude modulation-based intensity modulation and direct detection channel performance using eye diagram measurements.Experimental results show that the proposed technique can achieve a high accuracy in jointly monitoring modulation format,probabilistic shaping,roll-off factor,baud rate,optical signal-to-noise ratio,and chromatic dispersion.The designed VGG-based CNN model outperforms the other four traditional machine-learning methods in different scenarios.Furthermore,the multitask learning model combined with MobileNet CNN is designed to improve the flexibility of the network.Compared with the designed VGG-based CNN,the MobileNet-based MTL does not need to train all the classes,and it can simultaneously monitor single parameter or multiple parameters without sacrificing accuracy,indicating great potential in various monitoring scenarios.