Quantum Higher Order Singular Value Decomposition

被引：0
|
作者：
Gu, Lejia [1 ]
Wang, Xiaoqiang [1 ]
Zhang, Guofeng [1 ]
机构：
[1] Hong Kong Polytech Univ, Dept Appl Math, Hung Hom, Kowloon, Hong Kong, Peoples R China
来源：
2019 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC) | 2019年
关键词：
Quantum algorithm; Quantum machine learning; Higher order singular value decomposition (HOSVD); Tensor; ALGORITHMS;
D O I：
暂无
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
Higher order singular value decomposition (HOSVD) is an important tool for analyzing big data in multilinear algebra and machine learning. In this paper, we present a quantum algorithm for higher order singular value decomposition. Our method allows one to decompose a tensor into a core tensor containing tensor singular values and some unitary matrices by quantum computers. Compared to the classical HOSVD algorithm, our quantum algorithm provides an exponential speedup.
引用
收藏
页码：1166 / 1171
页数：6
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