机器学习在量子通信资源优化配置中的应用  

Application of machine learning in optimal allocation of quantum communication resources

作　　者：陈以鹏 刘靖阳 朱佳莉 方伟[1,2] 王琴[1,2] Chen Yi-Peng;Liu Jing-Yang;Zhu Jia-Li;Fang Wei;Wang Qin(Institute of Quantum Information and Technology,Nanjing University of Posts and Telecommunications,Nanjing 210003,China;Key Laboratory of Broadband Wireless Communication and Sensor Network of Ministry of Education,Nanjing University of Posts and Telecommunications,Nanjing 210003,China)

机构地区：[1]南京邮电大学,量子信息技术研究所,南京210003 [2]南京邮电大学,宽带无线通信与传感网教育部重点实验室,南京210003

出　　处：《物理学报》2022年第22期16-21,共6页Acta Physica Sinica

基　　金：国家重点研究发展计划(批准号:2018YFA0306400,2017YFA0304100);国家自然科学基金(批准号:12074194);江苏省自然科学基金前沿技术(批准号:BK20192001);江苏省研究生科研创新计划(批准号:KYCX20_0726)资助的课题。

摘　　要：在未来量子通信网络的大规模应用中,如何根据当前用户实际情况实现资源优化配置,比如选择最优量子密钥分发协议(quantum key distribution, QKD)和最优系统参数等,是实现网络应用的一个重要考察指标.传统的QKD最优协议选择以及参数优化配置方法,大多是通过局部搜索算法来实现.该方法需要花费大量的计算资源和时间.为此,本文提出了将机器学习算法应用到QKD资源优化配置之中,通过回归机器学习的方式来同时进行不同情境下的最优协议选择以及最优协议的参数优化配置.此外,将包括随机森林(random forest, RF)、最近邻(k-nearest neighbor, KNN)、逻辑回归(logistic regression)等在内的多种回归机器学习模型进行对比分析.数据仿真结果显示,基于机器学习的新方案与基于局部搜索算法的传统方案相比,在资源损耗方面实现了质的跨越,而且RF在多个回归评估指标上都取得了最佳的效果.此外,通过残差分析,发现以RF回归模型为代表的机器学习方案在最优协议选择以及参数优化配置方面具有很好的环境鲁棒性.因此,本工作将对未来量子通信网络实际应用起到重要的推进作用.In the application of quantum communication networks, it is an important task to realize the optimal allocation of resources according to the current situation. For example, We need to select the optimal quantum key distribution(QKD) protocol and parameters. Traditionally, the most commonly implemented method is the local search algorithm(LSA), which costs a lot of resources. Here in this work, we propose a machine learning based scheme, in which the regression machine learning is used to simultaneously select the optimal protocol and corresponding parameters. In addition, we make comparisons among a few machine learning models including random forest(RF), K-nearest neighbor(KNN) and logistic regression. Simulation results show that the new scheme takes much less time than the LSA scheme, and the RF achieves the best performance. In addition, through the RF residual analysis, we find that the machine learning scheme has good robustness. In conclusion, this work may play an important role in promoting the practical application of quantum communication networks.

关 键 词：量子通信 量子密钥分发 回归机器学习 资源优化配置 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] O413[自动化与计算机技术—控制科学与工程] TN918[理学—理论物理]