Experimental Machine Learning of Quantum States

Gao, Jun1,2; Qiao, Lu-Feng1,2; Jiao, Zhi-Qiang1,2; Ma, Yue-Chi3,4,5; Hu, Cheng-Qiu1,2; Ren, Ruo-Jing1,2; Yang, Ai-Lin1,2; Tang, Hao1,2; Yung, Man-Hong4,5,6; Jin, Xian-Min1,2

2018-06-11

10.1103/PhysRevLett.120.240501

PHYSICAL REVIEW LETTERS   影响因子和分区

0031-9007

1079-7114

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.

SCI ; EI

英语

NI论文

通讯

Science Technology and Innovation Commission of Shenzhen Municipality[ZDSYS20170303165926217] ; Science Technology and Innovation Commission of Shenzhen Municipality[JCYJ20170412152620376]

Physics

Physics, Multidisciplinary

WOS:000434766600001

AMER PHYSICAL SOC

20182505339069

Big data ; Classification (of information) ; Data mining ; Learning systems ; Neural networks

Information Theory and Signal Processing:716.1 ; Data Processing and Image Processing:723.2 ; Light/Optics:741.1

PHYSICS

Web of Science

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

Gao, Jun,Qiao, Lu-Feng,Jiao, Zhi-Qiang,et al. Experimental Machine Learning of Quantum States[J]. PHYSICAL REVIEW LETTERS,2018,120(24).

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).

Gao, Jun,et al."Experimental Machine Learning of Quantum States".PHYSICAL REVIEW LETTERS 120.24(2018).