Entanglement detection with classical deep neural networks Ureña, Julio Sojo, Antonio Bermejo Vega, Juan Manzano Diosdado, Daniel 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. 2024-09-05T08:30:54Z 2024-09-05T08:30:54Z 2024-08-05 journal article Ureña, J., Sojo, A., Bermejo-Vega, J. et al. Entanglement detection with classical deep neural networks. Sci Rep 14, 18109 (2024). https://doi.org/10.1038/s41598-024-68213-0 https://hdl.handle.net/10481/93976 10.1038/s41598-024-68213-0 eng info:eu-repo/grantAgreement/EC/NextGenerationEU/PID2021-128970OA-I00 http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Springer Nature