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Record |
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Author  |
Ureña, J.; Sojo, A.; Bermejo-Vega, J.; Manzano, D. |
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Title |
Entanglement detection with classical deep neural networks |
Type |
Journal Article |
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Year |
2024 |
Publication |
Scientific Reports |
Abbreviated Journal |
Sci Rep |
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Volume |
14 |
Issue |
1 |
Pages |
18109 - 11pp |
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Keywords |
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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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Address |
[Urena, Julio] CSIC, Inst Fis Corpuscular IF, Valencia 46980, Spain, Email: manzano@onsager.ugr.es |
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Publisher |
Nature Portfolio |
Place of Publication |
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Editor |
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Language |
English |
Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
2045-2322 |
ISBN |
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Expedition |
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Conference |
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Notes |
WOS:001284942100001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
no |
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Call Number |
IFIC @ pastor @ |
Serial |
6230 |
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