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Entanglement detection with artificial neural networks
Quantum entanglement is one of the essential resources involved in quantum information processing tasks. However, its detection for usage remains a challenge. The Bell-type inequality for relative entropy of coherence serves as an entanglement witness for pure entangled states. However, it does not...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9884245/ https://www.ncbi.nlm.nih.gov/pubmed/36709391 http://dx.doi.org/10.1038/s41598-023-28745-3 |
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author | Asif, Naema Khalid, Uman Khan, Awais Duong, Trung Q. Shin, Hyundong |
author_facet | Asif, Naema Khalid, Uman Khan, Awais Duong, Trung Q. Shin, Hyundong |
author_sort | Asif, Naema |
collection | PubMed |
description | Quantum entanglement is one of the essential resources involved in quantum information processing tasks. However, its detection for usage remains a challenge. The Bell-type inequality for relative entropy of coherence serves as an entanglement witness for pure entangled states. However, it does not perform reliably for mixed entangled states. This paper constructs a classifier by employing the relationship between coherence and entanglement for supervised machine learning methods. This method encodes multiple Bell-type inequalities for the relative entropy of coherence into an artificial neural network to detect the entangled and separable states in a quantum dataset. |
format | Online Article Text |
id | pubmed-9884245 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-98842452023-01-30 Entanglement detection with artificial neural networks Asif, Naema Khalid, Uman Khan, Awais Duong, Trung Q. Shin, Hyundong Sci Rep Article Quantum entanglement is one of the essential resources involved in quantum information processing tasks. However, its detection for usage remains a challenge. The Bell-type inequality for relative entropy of coherence serves as an entanglement witness for pure entangled states. However, it does not perform reliably for mixed entangled states. This paper constructs a classifier by employing the relationship between coherence and entanglement for supervised machine learning methods. This method encodes multiple Bell-type inequalities for the relative entropy of coherence into an artificial neural network to detect the entangled and separable states in a quantum dataset. Nature Publishing Group UK 2023-01-28 /pmc/articles/PMC9884245/ /pubmed/36709391 http://dx.doi.org/10.1038/s41598-023-28745-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Asif, Naema Khalid, Uman Khan, Awais Duong, Trung Q. Shin, Hyundong Entanglement detection with artificial neural networks |
title | Entanglement detection with artificial neural networks |
title_full | Entanglement detection with artificial neural networks |
title_fullStr | Entanglement detection with artificial neural networks |
title_full_unstemmed | Entanglement detection with artificial neural networks |
title_short | Entanglement detection with artificial neural networks |
title_sort | entanglement detection with artificial neural networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9884245/ https://www.ncbi.nlm.nih.gov/pubmed/36709391 http://dx.doi.org/10.1038/s41598-023-28745-3 |
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