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http://hdl.handle.net/10397/93313
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 |
Appears in Collections: | Conference Paper |
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