Variational quantum support vector machine based on Hadamard test  

作　　者：Li Xu Xiao-Yu Zhang Jin-Min Liang Jing Wang Ming Li Ling Jian Shu-qian Shen 

机构地区：[1]College of Science,China University of Petroleum,Qingdao 266580 China [2]School of Mathematical Sciences,Capital Normal University,Beijing 100048,China [3]School of Economics and Management,China University of Petroleum,Qingdao 266580,China

出　　处：《Communications in Theoretical Physics》2022年第5期61-69,共9页理论物理通讯（英文版）

基　　金：supported by the Shandong Provincial Natural Science Foundation for Quantum Science No.ZR2020LLZ003,ZR2021LLZ002。

摘　　要：Classical machine learning algorithms seem to be totally incapable of processing tremendous amounts of data,while quantum machine learning algorithms could deal with big data with ease and provide exponential acceleration over classical counterparts.Meanwhile,variational quantum algorithms are widely proposed to solve relevant computational problems on noisy,intermediate-scale quantum devices.In this paper,we apply variational quantum algorithms to quantum support vector machines and demonstrate a proof-of-principle numerical experiment of this algorithm.In addition,in the classification stage,fewer qubits,shorter circuit depth,and simpler measurement requirements show its superiority over the former algorithms.

关 键 词：quantum support vector machine Hadamard test variational quantum algorithm 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]