典型文献
Quantum partial least squares regression algorithm for multiple correlation problem
文献摘要：
Partial least squares(PLS)regression is an important linear regression method that efficiently addresses the multiple correlation problem by combining principal component analysis and multiple regression.In this paper,we present a quan-tum partial least squares(QPLS)regression algorithm.To solve the high time complexity of the PLS regression,we design a quantum eigenvector search method to speed up principal components and regression parameters construction.Mean-while,we give a density matrix product method to avoid multiple access to quantum random access memory(QRAM)during building residual matrices.The time and space complexities of the QPLS regression are logarithmic in the indepen-dent variable dimension n,the dependent variable dimension w,and the number of variables m.This algorithm achieves exponential speed-ups over the PLS regression on n,m,and w.In addition,the QPLS regression inspires us to explore more potential quantum machine learning applications in future works.
文献关键词：
中图分类号：
作者姓名：
Yan-Yan Hou;Jian Li;Xiu-Bo Chen;Yuan Tian
作者机构：
School of Artificial Intelligence,Beijing University of Post and Telecommunications,Beijing 100876,China;College of Information Science and Engineering,Zaozhuang University,Zaozhuang 277160,China;Information Security Center,State Key Laboratory of Networking and Switching Technology,Beijing University of Post and Telecommunications,Beijing 100876,China;GuiZhou University,Guizhou Provincial Key Laboratory of Public Big Data,Guiyang 550025,China
文献出处：
引用格式：
[1]Yan-Yan Hou;Jian Li;Xiu-Bo Chen;Yuan Tian-.Quantum partial least squares regression algorithm for multiple correlation problem)[J].中国物理B（英文版）,2022(03):198-207
A类：
QPLS,QRAM
B类：
Quantum,partial,least,squares,regression,algorithm,multiple,correlation,problem,Partial,important,linear,method,that,efficiently,addresses,by,combining,principal,analysis,In,this,paper,we,present,To,solve,high,complexity,design,quantum,eigenvector,search,speed,components,parameters,construction,Mean,while,give,density,matrix,product,avoid,access,random,memory,during,building,residual,matrices,space,complexities,logarithmic,indepen,dimension,dependent,number,variables,This,achieves,exponential,ups,over,addition,inspires,us,explore,more,potential,machine,learning,applications,future,works
AB值：
0.535466
相似文献
机标中图分类号，由域田数据科技根据网络公开资料自动分析生成，仅供学习研究参考。
