Quantum partial least squares regression algorithm for multiple correlation problem

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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.
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