Neural Network-Powered Nonlinear Compensation Framework for High-Speed Continuous Variable Quantum Key Distribution

In this paper, we present a neural network-based framework to compensate for the nonlinear distortion of high-speed continuous variable quantum key distribution (CV-QKD) systems. In practice, the nonlinear impairment of imperfect devices could bring interference to the measured quadrature values and thus compromise the parameter estimation procedure and the secure key rate. Specifically, for a high-speed CV-QKD system, the balanced homodyne detector (BHD) and the following analog-to-digital converter are the primary sources of nonlinear impairment. The nonlinear distortion effect will be more pronounced as the pulse repetition rate increases. We propose an autoencoder-based network to learn the inner patterns of distorted data and then compensate the quadrature measurements in real-time at the receiver’s side. The numerical simulation demonstrates that the excess noise induced by nonlinear impairment is reduced to the
$10^{-3}$
level in the shot noise unit. Consequently, such a software approach has the ability to significantly boost the bandwidth efficiency of the BHD and the secure key bit rate.