论文标题
机器学习有助于抑制过量的噪声,以进行连续变化的量子密钥分布
Machine Learning assisted excess noise suppression for continuous-variable quantum key distribution
论文作者
论文摘要
多余的噪声是高性能连续变量量子键分布(CVQKD)的主要障碍,该分布主要源自通道不稳定性引起的量子信号的振幅衰减和相位波动。在这里,提出了基于均衡的过量抑制噪声方案。在此方案中,可以通过神经网络和飞行员音调协助均衡来纠正扭曲的信号,从而减轻了后处理的压力并消除了硬件成本。对于具有更强烈波动的自由空间通道,添加了分类算法以对接收到的变量进行分类,然后对不同类别进行独特的均衡校正。实验结果表明,该方案可以将多余的噪声抑制至较低的水平,并具有显着的性能改善。此外,该方案还使系统能够应对强大的湍流。它打破了长距离量子通信的瓶颈,并为CVQKD的大规模应用奠定了基础。
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.