| Issue |
Wuhan Univ. J. Nat. Sci.
Volume 29, Number 3, June 2024
|
|
|---|---|---|
| Page(s) | 193 - 194 | |
| DOI | https://doi.org/10.1051/wujns/2024293193 | |
| Published online | 03 July 2024 | |
Correspondence
Exploring the Power of Entangled Data in Quantum Machine Learning
1
Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan 430072, Hubei, China
2
National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan 430072, Hubei, China
3
JD Explore Academy, Beijing 101111, China
4
School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore
5
School of Computer Science, Faculty of Engineering, University of Sydney, NSW 2008, Australia
6
Center on Frontiers of Computing Studies, Peking University, Beijing 100871, China
7
School of Computer Science, Peking University, Beijing 100871, China
† Corresponding author. E-mail: luoyong@whu.edu.cn
Received:
2
June
2024
This article has no abstract.
Cite this article: WANG Xinbiao, DU Yuxuan, TU Zhuozhuo, et al. Exploring the Power of Entangled Data in Quantum Machine Learning[J]. Wuhan Univ J of Nat Sci, 2024, 29(3): 193-194.
Biography: WANG Xinbiao, Ph.D. candidate, research direction: quantum machine learning algorithms and theory, and quantum information theory. E-mail: wangxb08@whu.edu.cn
Fundation item: LUO Yong acknowledges support from the National Natural Science Foundation of China (U23A20318 and 62276195). YUAN Xiao acknowledges support from the National Natural Science Foundation of China (12175003, 12361161602), NSAF (U2330201)
© Wuhan University 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.
