Entanglement detection with classical deep neural networks
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866913551719858176 |
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| author | Ureña, Julio Sojo, Antonio Bermejo, Juani Manzano, Daniel |
| author_facet | Ureña, Julio Sojo, Antonio Bermejo, Juani Manzano, Daniel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_05946 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Entanglement detection with classical deep neural networks Ureña, Julio Sojo, Antonio Bermejo, Juani Manzano, Daniel Quantum Physics 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. |
| title | Entanglement detection with classical deep neural networks |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2304.05946 |