@misc{10481/93976,
year = {2024},
month = {8},
url = {https://hdl.handle.net/10481/93976},
abstract = {In this study, we introduce an autonomous method for addressing the detection and classification of
quantum entanglement, a core element of quantum mechanics that has yet to be fully understood.
We employ a multi-layer perceptron to effectively identify entanglement in both two- and three-qubit
systems. Our technique yields impressive detection results, achieving nearly perfect accuracy for twoqubit
systems and over 90% accuracy for three-qubit systems. Additionally, our approach successfully
categorizes three-qubit entangled states into distinct groups with a success rate of up to 77%. These
findings indicate the potential for our method to be applied to larger systems, paving the way for
advancements in quantum information processing applications.},
organization = {Project PID2021-128970OA-I00 funded by MCIN/AEI/10.13039/501100011033 and, by “ERDF A way of making Europe”, by the “European Union”, the Ministry of
Economic Affairs and Digital Transformation of the Spanish Government through the QUANTUM ENIA
project call - Quantum Spain project, and by the European Union through the Recovery, Transformation and
Resilience Plan - NextGenerationEU within the framework of the Digital Spain 2026 Agenda and FEDER/Junta
de Andalucía program A.FQM.752.UGR20},
publisher = {Springer Nature},
title = {Entanglement detection with classical deep neural networks},
doi = {10.1038/s41598-024-68213-0},
author = {Ureña, Julio and Sojo, Antonio and Bermejo Vega, Juan and Manzano Diosdado, Daniel},
}