Parameter Optimization of Continuous-Variable Quantum Key Distribution Based on Ga-Bp Neural Network

In the continuous-variable quantum key distribution (CV-QKD) system, the secret key rate (SKR) and transmission distance are important factors to evaluate the quality of system. Numerical methods are commonly employed to compute SKR of QKD systems in practice, but it is difficult to find the optimal parameter to enhance SKR. In this paper, we propose a machine learning-based parameter optimization scheme of CV-QKD. The proposed scheme applies the genetic algorithm-back propagation neural network (GA-BPNN) to CV-QKD to learn the mapping relationship between SKR and modulation variance. According to the property that the mapping relationship is a concave function, the optimal modulation variance can be determined by bisection, and then SKR can be enhanced. Simulation results demonstrate that the proposed scheme can enhance SKR and transmission distance.