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arXiv · 2304.05946

Entanglement detection with classical deep neural networks

Julio Ureña·Antonio Sojo·Juani Bermejo·Daniel Manzano
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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 two-qubit 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.

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Julio Ureña, Antonio Sojo, Juani Bermejo, Daniel Manzano. 2023-04-12. Entanglement detection with classical deep neural networks. https://doi.org/10.1038/s41598-024-68213-0

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