Machine Learning Experimental Multipartite Entanglement Structure

Tian，Yu1,2,3; Che，Liangyu1,2,3; Long，Xinyue1,2,3; Ren，Changliang4; Lu，Dawei1,2,3

2022

10.1002/qute.202200025

Advanced Quantum Technologies   Impact Factor & Quartile

2511-9044

With the rapid growth of controllable qubits in recent years, experimental multipartite entangled states can be created with high fidelity in various moderate- and large-scale physical systems. However, the characterization of multipartite entanglement structure remains a formidable challenge, as traditionally it requires exponential number of local measurements to realize the identification. Machine learning is demonstrated to be an efficient tool to detect the underlying entanglement structure for ideal states, but it has non-negligible underperformance when tackling imperfect experimental data in reality. Here, a modified classifier based on feed-forward neural network to predict experimental entanglement structure in terms of entanglement intactness and depth is proposed. By preprocessing the input data, the classifier maintains efficiency and reliability against experimental noises, with the accuracy being enhanced from 69.7% to 91.2% for 6-qubit entangled states in spin systems. This method is anticipated to shed light on future studies of entanglement structure, in particular when the number of controlled qubits reaches explosive growth in practice.

entanglement structure machine learning nuclear magnetic resonance

SCI ; EI

English

First ; Corresponding

National Key Research and Development Program of China["2019YFA0308100","2017YFA0305000"] ; National Natural Science Foundation of China["12075110","12075245","11975117","11905099","11875159","11905111","U1801661"] ; Guangdong Basic and Applied Basic Research Foundation[2019A1515011383] ; Guangdong International Collaboration Program[2020A0505100001] ; Science, Technology and Innovation Commission of Shenzhen Municipality["ZDSYS20190902092905285","KQTD20190929173815000","JCYJ20200109140803865","JCYJ20180302174036418"] ; Pengcheng Scholars, Guangdong Innovative and Entrepreneurial Research Team Program[2019ZT08C044] ; Guangdong Provincial Key Laboratory[2019B121203002] ; Natural Science Foundation of Hunan Province[2021JJ10033]

Physics ; Optics

Quantum Science & Technology ; Optics

WOS:000837148300001

WILEY

20223212551269

Feedforward neural networks ; Quantum entanglement ; Qubits

Artificial Intelligence:723.4 ; Light, Optics and Optical Devices:741 ; Nanotechnology:761 ; Quantum Theory; Quantum Mechanics:931.4

2-s2.0-85135527817

Scopus

1.Department of Physics and Shenzhen Institute for Quantum Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
2.Shenzhen Key Laboratory of Advanced Quantum Functional Materials and Devices,Southern University of Science and Technology,Shenzhen,518055,China
3.Guangdong Provincial Key Laboratory of Quantum Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
4.Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education,Key Laboratory for Matter Microstructure and Function of Hunan Province,Department of Physics and Synergetic Innovation Center for Quantum Effects and Applications,Hunan Normal University,Changsha,410081,China

Tian，Yu,Che，Liangyu,Long，Xinyue,et al. Machine Learning Experimental Multipartite Entanglement Structure[J]. Advanced Quantum Technologies,2022.

Tian，Yu,Che，Liangyu,Long，Xinyue,Ren，Changliang,&Lu，Dawei.(2022).Machine Learning Experimental Multipartite Entanglement Structure.Advanced Quantum Technologies.

Tian，Yu,et al."Machine Learning Experimental Multipartite Entanglement Structure".Advanced Quantum Technologies (2022).