中文版 | English
题名

Experimental Machine Learning of Quantum States

作者
通讯作者Yung, Man-Hong; Jin, Xian-Min
发表日期
2018-06-11
DOI
发表期刊
ISSN
0031-9007
EISSN
1079-7114
卷号120期号:24
摘要
Quantum information technologies provide promising applications in communication and computation, while machine learning has become a powerful technique for extracting meaningful structures in "big data." A crossover between quantum information and machine learning represents a new interdisciplinary area stimulating progress in both fields. Traditionally, a quantum state is characterized by quantum-state tomography, which is a resource-consuming process when scaled up. Here we experimentally demonstrate a machine-learning approach to construct a quantum-state classifier for identifying the separability of quantum states. We show that it is possible to experimentally train an artificial neural network to efficiently learn and classify quantum states, without the need of obtaining the full information of the states. We also show how adding a hidden layer of neurons to the neural network can significantly boost the performance of the state classifier. These results shed new light on how classification of quantum states can be achieved with limited resources, and represent a step towards machine-learning-based applications in quantum information processing.
相关链接[来源记录]
收录类别
SCI ; EI
语种
英语
重要成果
NI论文
学校署名
通讯
资助项目
Science Technology and Innovation Commission of Shenzhen Municipality[ZDSYS20170303165926217] ; Science Technology and Innovation Commission of Shenzhen Municipality[JCYJ20170412152620376]
WOS研究方向
Physics
WOS类目
Physics, Multidisciplinary
WOS记录号
WOS:000434766600001
出版者
EI入藏号
20182505339069
EI主题词
Big data ; Classification (of information) ; Data mining ; Learning systems ; Neural networks
EI分类号
Information Theory and Signal Processing:716.1 ; Data Processing and Image Processing:723.2 ; Light/Optics:741.1
ESI学科分类
PHYSICS
来源库
Web of Science
引用统计
被引频次[WOS]:108
成果类型期刊论文
条目标识符http://sustech.caswiz.com/handle/2SGJ60CL/27611
专题量子科学与工程研究院
理学院_物理系
作者单位
1.Shanghai Jiao Tong Univ, Dept Phys & Astron, State Key Lab Adv Opt Commun Syst & Networks, Shanghai 200240, Peoples R China
2.Univ Sci & Technol China, Synerget Innovat Ctr Quantum Informat & Quantum P, Hefei 230026, Anhui, Peoples R China
3.Tsinghua Univ, Inst Interdisciplinary Informat Sci, Ctr Quantum Informat, Beijing 100084, Peoples R China
4.Southern Univ Sci & Technol, Inst Quantum Sci & Engn, Shenzhen 518055, Peoples R China
5.Southern Univ Sci & Technol, Dept Phys, Shenzhen 518055, Peoples R China
6.Shenzhen Key Lab Quantum Sci & Engn, Shenzhen 518055, Peoples R China
通讯作者单位量子科学与工程研究院;  物理系
推荐引用方式
GB/T 7714
Gao, Jun,Qiao, Lu-Feng,Jiao, Zhi-Qiang,et al. Experimental Machine Learning of Quantum States[J]. PHYSICAL REVIEW LETTERS,2018,120(24).
APA
Gao, Jun.,Qiao, Lu-Feng.,Jiao, Zhi-Qiang.,Ma, Yue-Chi.,Hu, Cheng-Qiu.,...&Jin, Xian-Min.(2018).Experimental Machine Learning of Quantum States.PHYSICAL REVIEW LETTERS,120(24).
MLA
Gao, Jun,et al."Experimental Machine Learning of Quantum States".PHYSICAL REVIEW LETTERS 120.24(2018).
条目包含的文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可 操作
Gao-2018-Experimenta(2140KB)----限制开放--
个性服务
原文链接
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
导出为Excel格式
导出为Csv格式
Altmetrics Score
谷歌学术
谷歌学术中相似的文章
[Gao, Jun]的文章
[Qiao, Lu-Feng]的文章
[Jiao, Zhi-Qiang]的文章
百度学术
百度学术中相似的文章
[Gao, Jun]的文章
[Qiao, Lu-Feng]的文章
[Jiao, Zhi-Qiang]的文章
必应学术
必应学术中相似的文章
[Gao, Jun]的文章
[Qiao, Lu-Feng]的文章
[Jiao, Zhi-Qiang]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
[发表评论/异议/意见]
暂无评论

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。