Genuine multipartite entanglement verification with convolutional neural networks
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866916907745017856 |
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| author | Luo, Yi-Jun Leng, Xuan Zhang, Chengjie |
| author_facet | Luo, Yi-Jun Leng, Xuan Zhang, Chengjie |
| contents | In recent years, the detection of genuine multipartite entanglement (GME) via machine learning has received scant attention. Here, we employ convolutional neural networks (CNNs), as well as CNNs enhanced with squeeze-and-excitation (SE) to detect GME. We randomly generated GME states with 4 to 6 qubits and GHZ-diagonal states ranging from 4 to 20 qubits using the semidefinite programming approach. Subsequently, we assessed their classification accuracy. Our results demonstrate that the integration of the SE module significantly improved training performance. Additionally, we conducted an analysis of false positive and false negative occurrences. Utilizing our training data, we have substantially reduced the likelihood of incorrectly classifying non-entangled states as entangled. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_13463 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Genuine multipartite entanglement verification with convolutional neural networks Luo, Yi-Jun Leng, Xuan Zhang, Chengjie Quantum Physics In recent years, the detection of genuine multipartite entanglement (GME) via machine learning has received scant attention. Here, we employ convolutional neural networks (CNNs), as well as CNNs enhanced with squeeze-and-excitation (SE) to detect GME. We randomly generated GME states with 4 to 6 qubits and GHZ-diagonal states ranging from 4 to 20 qubits using the semidefinite programming approach. Subsequently, we assessed their classification accuracy. Our results demonstrate that the integration of the SE module significantly improved training performance. Additionally, we conducted an analysis of false positive and false negative occurrences. Utilizing our training data, we have substantially reduced the likelihood of incorrectly classifying non-entangled states as entangled. |
| title | Genuine multipartite entanglement verification with convolutional neural networks |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2508.13463 |