Separability-entanglement classifier via machine learning

Lu, Sirui1; Huang, Shilin2,3; Li, Keren1,2; Li, Jun2,4,5,6; Chen, Jianxin7; Lu, Dawei2,4,5; Ji, Zhengfeng8,9; Shen, Yi10; Zhou, Duanlu11; Zeng, Bei2,4,5,6

2018-07-13

10.1103/PhysRevA.98.012315

PHYSICAL REVIEW A   影响因子和分区

2469-9926

2469-9934

The problem of determining whether a given quantum state is entangled lies at the heart of quantum information processing. Despite the many methods-such as the positive partial transpose criterion and the k-symmetric extendibility criterion-to tackle this problem, none of them enables a general, practical solution due to the problem's NP-hard complexity. Explicitly, separable states form a high-dimensional convex set of vastly complicated structures. In this work, we build a different separability-entanglement classifier underpinned by machine learning techniques. We use standard tools from machine learning to learn the entanglement feature of arbitrary given quantum states. We perform substantial numerical tests on two-qubit and two-qutrit systems, and the results indicate that our method can outperform the existing methods in generic cases in terms of both speed and accuracy. This opens up avenues to explore quantum entanglement via the machine learning approach.

SCI ; EI

英语

通讯

National Key Research and Development Program of China[2016YFA0300603]

Optics ; Physics

Optics ; Physics, Atomic, Molecular & Chemical

WOS:000438489900006

AMER PHYSICAL SOC

20183005581927

Artificial intelligence ; Computational complexity ; Learning systems ; Numerical methods ; Quantum optics ; Set theory

Computer Theory, Includes Formal Logic, Automata Theory, Switching Theory, Programming Theory:721.1 ; Artificial Intelligence:723.4 ; Combinatorial Mathematics, Includes Graph Theory, Set Theory:921.4 ; Numerical Methods:921.6 ; Quantum Theory; Quantum Mechanics:931.4

PHYSICS

Web of Science

1.Tsinghua Univ, Dept Phys, Beijing 100084, Peoples R China
2.Univ Waterloo, Inst Quantum Comp, Waterloo, ON N2L 3G1, Canada
3.Tsinghua Univ, Inst Interdisciplinary Informat Sci, 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.Univ Guelph, Dept Math & Stat, Guelph, ON N1G 2W1, Canada
7.Univ Maryland, Joint Ctr Quantum Informat & Comp Sci, College Pk, MD 20742 USA
8.Univ Technol Sydney, Fac Engn & Informat Technol, Sch Software, Ctr Quantum Software & Informat, Sydney, NSW, Australia
9.Chinese Acad Sci, Inst Software, State Key Lab Comp Sci, Beijing, Peoples R China
10.Univ Waterloo, Dept Stat & Actuarial Sci, Waterloo, ON N2L 3G1, Canada
11.Chinese Acad Sci, Inst Phys, Beijing 100190, Peoples R China

Lu, Sirui,Huang, Shilin,Li, Keren,et al. Separability-entanglement classifier via machine learning[J]. PHYSICAL REVIEW A,2018,98(1).

Lu, Sirui.,Huang, Shilin.,Li, Keren.,Li, Jun.,Chen, Jianxin.,...&Zeng, Bei.(2018).Separability-entanglement classifier via machine learning.PHYSICAL REVIEW A,98(1).

Lu, Sirui,et al."Separability-entanglement classifier via machine learning".PHYSICAL REVIEW A 98.1(2018).