TY - JOUR
T1 - Detecting quantum entanglement with unsupervised learning
AU - Chen, Yiwei
AU - Pan, Yu
AU - Zhang, Guofeng
AU - Cheng, Shuming
N1 - Funding Information:
This research was supported by the National Natural Science Foundation of China under Grants Nos. 62173296 and 62088101. G F also acknowledges support from Hong Kong Research Grant Council (Grants Nos. 1520841, 15203619, and 15506619), Shenzhen Fundamental Research Fund, China, under Grant No. JCYJ20190813165207290, and the CAS AMSS-polyU Joint Laboratory of Applied Mathematics.
Publisher Copyright:
© 2021 The Author(s). Published by IOP Publishing Ltd
PY - 2022/1
Y1 - 2022/1
N2 - Quantum properties, such as entanglement and coherence, are indispensable resources in various quantum information processing tasks. However, there still lacks an efficient and scalable way to detecting these useful features especially for high-dimensional and multipartite quantum systems. In this work, we exploit the convexity of samples without the desired quantum features and design an unsupervised machine learning method to detect the presence of such features as anomalies. Particularly, in the context of entanglement detection, we propose a complex-valued neural network composed of pseudo-siamese network and generative adversarial net, and then train it with only separable states to construct non-linear witnesses for entanglement. It is shown via numerical examples, ranging from two-qubit to ten-qubit systems, that our network is able to achieve high detection accuracy which is above 97.5% on average. Moreover, it is capable of revealing rich structures of entanglement, such as partial entanglement among subsystems. Our results are readily applicable to the detection of other quantum resources such as Bell nonlocality and steerability, and thus our work could provide a powerful tool to extract quantum features hidden in multipartite quantum data.
AB - Quantum properties, such as entanglement and coherence, are indispensable resources in various quantum information processing tasks. However, there still lacks an efficient and scalable way to detecting these useful features especially for high-dimensional and multipartite quantum systems. In this work, we exploit the convexity of samples without the desired quantum features and design an unsupervised machine learning method to detect the presence of such features as anomalies. Particularly, in the context of entanglement detection, we propose a complex-valued neural network composed of pseudo-siamese network and generative adversarial net, and then train it with only separable states to construct non-linear witnesses for entanglement. It is shown via numerical examples, ranging from two-qubit to ten-qubit systems, that our network is able to achieve high detection accuracy which is above 97.5% on average. Moreover, it is capable of revealing rich structures of entanglement, such as partial entanglement among subsystems. Our results are readily applicable to the detection of other quantum resources such as Bell nonlocality and steerability, and thus our work could provide a powerful tool to extract quantum features hidden in multipartite quantum data.
KW - Entanglement detection
KW - Machine learning
KW - Quantum resource
UR - http://www.scopus.com/inward/record.url?scp=85119506155&partnerID=8YFLogxK
U2 - 10.1088/2058-9565/ac310f
DO - 10.1088/2058-9565/ac310f
M3 - Journal article
AN - SCOPUS:85119506155
SN - 2058-9565
VL - 7
SP - 1
EP - 12
JO - Quantum Science and Technology
JF - Quantum Science and Technology
IS - 1
M1 - 015005
ER -