改进的测量设备无关协议参数优化方法  

Improved parameter optimization method for measurement device independent protocol

作　　者：周江平[1] 周媛媛[1] 周学军[1] Zhou Jiang-Ping;Zhou Yuan-Yuan;Zhou Xue-Jun(College of Electronic Engineering,Naval University of Engineering Wuhan 430000,China)

机构地区：[1]海军工程大学电子工程学院,武汉430000

出　　处：《物理学报》2023年第12期46-53,共8页Acta Physica Sinica

摘　　要：实际量子密钥分发中参数的优化选择能大幅提升系统密钥生成率和最大传输距离,由于全局搜索算法的成本过大,本地搜索算法被广泛地应用.然而该算法存在两个问题,一是所得解不一定为全局最优解,二是算法的有效性极大地受制于初始值的选择.利用蒙特卡罗方法对密钥生成率函数是否为凸函数进行了证明,并仿真分析了密钥生成率函数在不同参数维度上的特性,提出了粒子群本地搜索算法并与本地搜索算法进行仿真比较.结果表明,密钥生成率函数为非凸函数,但合理设置初始值,本地搜索算法仍能求得全局最优解;在传输距离较远时,本地搜索算法因难以通过随机取值的方法得到有效的初始值而失效,粒子群本地搜索算法能克服这一缺点,以轻微增加算法复杂度为代价,提升了系统的最大传输距离.The optimal selection of parameters in practical quantum key distribution can greatly improve the key generation rate and maximum transmission distance of the system.Owing to the high cost of global search algorithm,local search algorithm is widely used.However,there are two shortcomings in local search algorithm.One is that the solution obtained is not always the global optimal solution,and the other is that the effectiveness of the algorithm is greatly dependent on the choice of initial value.This paper uses the Monte Carlo method to prove whether the key generation rate function is convex,and also simulates and analyzes the projection of the key generation rate function on each dimension of the parameter,in contrast to the approach in previous article.In order to eliminate the effect of the initial value,this paper proposes the particle swarm local search optimization algorithm which integrates particle swarm optimization algorithm and local search algorithm.The first step is to use the particle swarm optimization to find a valid parameter which leads to nonzero key generation rate,and the second step is to adopt the parameter as the initial value of local search algorithm to derive the global optimal solution.Then,the two algorithms are used to conduct simulation and their results are compared.The results show that the key generation rate function is non-convex because it does not satisfy the definition of a convex function,however,since the key generation rate function has only one non-zero stagnation point,the LSA algorithm can still obtain the global optimal solution with an appropriate initial value.When the transmission distance is relatively long,the local search algorithm is invalid because it is difficult to obtain an effective initial value by random value method.The particle swarm optimization algorithm can overcome this shortcoming and improve the maximum transmission distance of the system at the cost of slightly increasing the complexity of the algorithm.

关 键 词：量子密钥分发 粒子群算法 测量设备无关协议 参数优化 

分 类 号：TN918.4[电子电信—通信与信息系统] O413[电子电信—信息与通信工程]