Entanglement detection with artificial neural networks

Naema Asif; Uman Khalid; Awais Khan; Trung Q Duong; Hyundong Shin

doi:10.1038/s41598-023-28745-3

Entanglement detection with artificial neural networks

Naema Asif, Uman Khalid, Awais Khan, Trung Q Duong, Hyundong Shin

School of Electronics, Electrical Engineering and Computer Science

Research output: Contribution to journal › Article › peer-review

2 Downloads (Pure)

Abstract

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. [Abstract copyright: © 2023. The Author(s).]

Original language	English
Article number	1562
Number of pages	8
Journal	Scientific Reports
Volume	13
Issue number	1
DOIs	https://doi.org/10.1038/s41598-023-28745-3
Publication status	Published - 28 Jan 2023

Access to Document

10.1038/s41598-023-28745-3Licence: CC BY

Entanglement detection with artifcial neural networks
© 2023 The Authors. This is an open access article published under a Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited.
Final published version, 1.27 MBLicence: CC BY

Cite this

@article{e43e8e2f8a5442e3afb7c2c486325f80,

title = "Entanglement detection with artificial neural networks",

abstract = "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. [Abstract copyright: {\textcopyright} 2023. The Author(s).]",

author = "Naema Asif and Uman Khalid and Awais Khan and Duong, {Trung Q} and Hyundong Shin",

year = "2023",

month = jan,

day = "28",

doi = "10.1038/s41598-023-28745-3",

language = "English",

volume = "13",

journal = "Scientific Reports",

issn = "2045-2322",

publisher = "Nature Publishing Group",

number = "1",

}

TY - JOUR

T1 - Entanglement detection with artificial neural networks

AU - Asif, Naema

AU - Khalid, Uman

AU - Khan, Awais

AU - Duong, Trung Q

AU - Shin, Hyundong

PY - 2023/1/28

Y1 - 2023/1/28

N2 - 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. [Abstract copyright: © 2023. The Author(s).]

AB - 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. [Abstract copyright: © 2023. The Author(s).]

U2 - 10.1038/s41598-023-28745-3

DO - 10.1038/s41598-023-28745-3

M3 - Article

C2 - 36709391

VL - 13

JO - Scientific Reports

JF - Scientific Reports

SN - 2045-2322

IS - 1

M1 - 1562

ER -

Entanglement detection with artificial neural networks

Abstract

Access to Document

Fingerprint

Cite this