Entanglement detection with artificial neural networks

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

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)
60 Downloads (Pure)


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 languageEnglish
Article number1562
Number of pages8
JournalScientific Reports
Issue number1
Publication statusPublished - 28 Jan 2023


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