Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/95432
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Applied Mathematics | en_US |
dc.contributor | Mainland Development Office | en_US |
dc.creator | Chen, Y | en_US |
dc.creator | Pan, Y | en_US |
dc.creator | Zhang, G | en_US |
dc.creator | Cheng, S | en_US |
dc.date.accessioned | 2022-09-19T02:00:53Z | - |
dc.date.available | 2022-09-19T02:00:53Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/95432 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Physics Publishing Ltd. | en_US |
dc.rights | ©2021The Author(s). Published by IOP Publishing Ltd | en_US |
dc.rights | Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence (https://creativecommons.org/licenses/by/4.0/). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. | en_US |
dc.rights | The following publication Chen, Y., Pan, Y., Zhang, G., & Cheng, S. (2021). Detecting quantum entanglement with unsupervised learning. Quantum Science and Technology, 7(1), 015005 is available at https://doi.org/10.1088/2058-9565/ac310f. | en_US |
dc.subject | Entanglement detection | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Quantum resource | en_US |
dc.title | Detecting quantum entanglement with unsupervised learning | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 7 | en_US |
dc.identifier.issue | 1 | en_US |
dc.identifier.doi | 10.1088/2058-9565/ac310f | en_US |
dcterms.abstract | 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. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Quantum science and technology, Jan. 2022, v. 7, no. 1, 15005 | en_US |
dcterms.isPartOf | Quantum science and technology | en_US |
dcterms.issued | 2022-01 | - |
dc.identifier.scopus | 2-s2.0-85119506155 | - |
dc.identifier.ros | 2021003470 | - |
dc.identifier.eissn | 2058-9565 | en_US |
dc.identifier.artn | 15005 | en_US |
dc.description.validate | 202209 bchy | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | CDCF_2021-2022 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Natural Science Foundation of China; Shenzhen Fundamental Research Fund, China; CAS AMSS-polyU Joint Laboratory of Applied Mathematics | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 68955815 | - |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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Chen_Detecting_quantum_entanglement.pdf | 2.45 MB | Adobe PDF | View/Open |
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