Machine Learning assisted excess noise suppression for continuous-variable quantum key distribution

TL;DR

This paper introduces a neural network-based equalization scheme to suppress excess noise in continuous-variable quantum key distribution, improving performance and enabling long-distance quantum communication.

Contribution

It proposes a novel neural network-assisted equalization method with classification for turbulence, enhancing CVQKD robustness against channel fluctuations.

Findings

Significant reduction in excess noise levels.

Improved performance in turbulent free-space channels.

Enabling long-distance quantum communication.

Abstract

Excess noise is a major obstacle to high-performance continuous-variable quantum key distribution (CVQKD), which is mainly derived from the amplitude attenuation and phase fluctuation of quantum signals caused by channel instability. Here, an excess noise suppression scheme based on equalization is proposed. In this scheme, the distorted signals can be corrected through equalization assisted by a neural network and pilot tone, relieving the pressure on the post-processing and eliminating the hardware cost. For a free-space channel with more intense fluctuation, a classification algorithm is added to classify the received variables, and then the distinctive equalization correction for different classes is carried out. The experimental results show that the scheme can suppress the excess noise to a lower level, and has a significant performance improvement. Moreover, the scheme also enables the system to cope with strong turbulence. It breaks the bottleneck of long-distance quantum communication and lays a foundation for the large-scale application of CVQKD.