基于GMM的幅度相位联合编码CVQKD安全性分析

GMM-based amplitude-phase joint coding CVQKD security analysis

为了提高离散调制连续变量量子密钥分发协议性能,采用幅度相移键控(APSK)联合调制格式方法,在接收端采用高斯混合模型分类算法识别量子态来提升系统的性能。将密钥传输系统分为状态学习和状态预测两个阶段,在状态学习阶段基于高斯混合模型的分类器对已知类别的量子态进行训练,学习不同类别量子态的幅度相位分布情况;在状态预测阶段则采用最小欧氏距离计算出待测量子态属于每个类别的后验概率,从而判定待测量子态的类别,并通过参数估计、反向协调和保密增强生成最终密钥。结果表明,在反向协调和集体攻击下128-APSK离散调制连续变量量子密钥分发协议能够有效生成安全密钥,当安全码率为10-6 bit/symbol时,传输距离可接近60 km。该研究为进一步提高离散调制连续变量量子密钥分发协议的系统性能提供了参考。

For the purpose of improving the performance of discrete modulation-continuous variable quantum key distribution(CVQKD)protocols,the amplitude phase-shift keying(APSK)modulation format method was used,proposed to use Gaussian mixed model(GMM)classification algorithm at the receiver side to identify quantum states to enhance the performance of the system,dividing the key transmission system into two stages:state learning and state prediction.In the state learning stage,the classifier based on GMM was trained for known classes of quantum states,learning the amplitude-phase distribution of different classes of quantum states.The state prediction stage then used the minimum Euclidean distance to calculate the posterior probability that the quantum state to be measured belongs to each class,thus determining the class of the quantum state to be measured,and generating the final key by parameter estimation,reverse coordination and secrecy enhancement.Numerical simulation results show that the 128-APSK discrete modulation continuous-variable quantum key distribution protocol can effectively generate secure keys under reverse coordination and collective attack,and the transmission distance can approach 60 km when the secure code rate is 10-6 bit/symbol.This study provides a reference for further improving the system performance of discrete modulation continuous-variable quantum key distribution protocols.