Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/93313
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Title: Quantum higher order singular value decomposition
Authors: Gu, L 
Wang, X 
Zhang, G 
Issue Date: 2019
Source: In Proceedings of 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), 6-9 October 2019, Bari, Italy, p. 1166-1171
Abstract: 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.
Keywords: Higher order singular value decomposition (HOSVD)
Quantum algorithm
Quantum machine learning
Tensor
Publisher: IEEE
ISBN: 978-1-7281-4569-3 (Electronic ISBN)
DOI: 10.1109/SMC.2019.8914525
Rights: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
The following publication Gu, L., Wang, X., & Zhang, G. (2019, October). Quantum higher order singular value decomposition. In 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) (pp. 1166-1171). IEEE is available at https://doi.org/10.1109/SMC.2019.8914525
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