Quantum hyperparallel algorithm for matrix multiplication
Hyperentangled states, entangled states with more than one degree of freedom, are considered as promising resource in quantum computation. Here we present a hyperparallel quantum algorithm for matrix multiplication with time complexity O(N2), which is better than the best known classical algorithm....
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| Format: | Online |
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Nature Publishing Group
2016
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4850379/ |
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pubmed-48503792016-05-05 Quantum hyperparallel algorithm for matrix multiplication Zhang, Xin-Ding Zhang, Xiao-Ming Xue, Zheng-Yuan Article Hyperentangled states, entangled states with more than one degree of freedom, are considered as promising resource in quantum computation. Here we present a hyperparallel quantum algorithm for matrix multiplication with time complexity O(N2), which is better than the best known classical algorithm. In our scheme, an N dimensional vector is mapped to the state of a single source, which is separated to N paths. With the assistance of hyperentangled states, the inner product of two vectors can be calculated with a time complexity independent of dimension N. Our algorithm shows that hyperparallel quantum computation may provide a useful tool in quantum machine learning and “big data” analysis. Nature Publishing Group 2016-04-29 /pmc/articles/PMC4850379/ /pubmed/27125586 http://dx.doi.org/10.1038/srep24910 Text en Copyright © 2016, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Zhang, Xin-Ding Zhang, Xiao-Ming Xue, Zheng-Yuan |
| spellingShingle |
Zhang, Xin-Ding Zhang, Xiao-Ming Xue, Zheng-Yuan Quantum hyperparallel algorithm for matrix multiplication |
| author_facet |
Zhang, Xin-Ding Zhang, Xiao-Ming Xue, Zheng-Yuan |
| author_sort |
Zhang, Xin-Ding |
| title |
Quantum hyperparallel algorithm for matrix multiplication |
| title_short |
Quantum hyperparallel algorithm for matrix multiplication |
| title_full |
Quantum hyperparallel algorithm for matrix multiplication |
| title_fullStr |
Quantum hyperparallel algorithm for matrix multiplication |
| title_full_unstemmed |
Quantum hyperparallel algorithm for matrix multiplication |
| title_sort |
quantum hyperparallel algorithm for matrix multiplication |
| description |
Hyperentangled states, entangled states with more than one degree of freedom, are considered as promising resource in quantum computation. Here we present a hyperparallel quantum algorithm for matrix multiplication with time complexity O(N2), which is better than the best known classical algorithm. In our scheme, an N dimensional vector is mapped to the state of a single source, which is separated to N paths. With the assistance of hyperentangled states, the inner product of two vectors can be calculated with a time complexity independent of dimension N. Our algorithm shows that hyperparallel quantum computation may provide a useful tool in quantum machine learning and “big data” analysis. |
| publisher |
Nature Publishing Group |
| publishDate |
2016 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4850379/ |
| _version_ |
1613573048140562432 |