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所属单位：计算机科学与技术学院/人工智能学院/软件学院
发表刊物：PHYSICAL REVIEW A
摘要：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号：2469-9926
是否译文：否
发表时间：2017-09-01
合写作者：Duan, Bojia,刘莺,李丹
通讯作者：袁家斌