Affiliation of Author(s):计算机科学与技术学院/人工智能学院/软件学院

Journal:PHYSICAL REVIEW A

Abstract:We propose a quantum algorithm for support matrix machines (SMMs) that efficiently addresses an image classification problem by introducing a least-squares reformulation. This algorithm consists of two core subroutines: a quantum matrix inversion (Harrow-Hassidim-Lloyd, HHL) algorithm and a quantum singular value thresholding (QSVT) algorithm. The two algorithms can be implemented on a universal quantum computer with complexity O[log (npq)] and O[log (pq)], respectively, where n is the number of the training data and pq is the size of the feature space. By iterating the algorithms, we can find the parameters for the SMM classfication model. Our analysis shows that both HHL and QSVT algorithms achieve an exponential increase of speed over their classical counterparts.

ISSN No.:2469-9926

Translation or Not:no

Date of Publication:2017-09-01

Co-author:Duan, Bojia,lz,Li Dan

Correspondence Author:Yuan Jiabing