Neural network method: withstanding noise for continuous-variable quantum key distribution with discrete modulation

Abstract

Excess noise in continuous-variable quantum key distribution systems usually results in a loss of key rate, leading to fatal security breaches. This paper proposes a long short-term memory time-sequence neural network to predict the key rate of the system while counteracting the effects of excess noise. The proposed network model, which can be updated with historical data, predicts the key rate of the future moment for the input time-sequence data. To increase the key rate, we perform a postselection operation to combat excess noise. We demonstrate the asymptotic security of the protocol against collective attacks with the numerical simulations using the quadrature phase-shift keying protocol, where some parameters have been optimized to resist excess noise. It provides a potential solution for improving the security of quantum communication in practical applications.