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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2601.22253 |
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| _version_ | 1866908798984126464 |
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| author | Muñoz-Mellado, Katherine Uzcátegui-Contreras, Daniel Guerra, Antonio Delgado, Aldo Goyeneche, Dardo |
| author_facet | Muñoz-Mellado, Katherine Uzcátegui-Contreras, Daniel Guerra, Antonio Delgado, Aldo Goyeneche, Dardo |
| contents | In this work, we propose a deep learning-based approach for quantum entanglement and discord classification using convolutional autoencoders. We train models to distinguish entangled from separable bipartite states for $d \times d$ systems with local dimension $d$ ranging from two to seven, which enables identification of bound and free entanglement. Through extensive numerical simulations across various quantum state families, we demonstrate that our model achieves high classification accuracy. Furthermore, we leverage the learned representations to generate samples of bound entangled states, the rarest form of entanglement and notoriously difficult to construct analytically. We separately train the same convolutional autoencoders architecture for detecting the presence of quantum discord and show that the model also exhibits high accuracy while requiring significantly less training time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_22253 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Entanglement and discord classification via deep learning Muñoz-Mellado, Katherine Uzcátegui-Contreras, Daniel Guerra, Antonio Delgado, Aldo Goyeneche, Dardo Quantum Physics In this work, we propose a deep learning-based approach for quantum entanglement and discord classification using convolutional autoencoders. We train models to distinguish entangled from separable bipartite states for $d \times d$ systems with local dimension $d$ ranging from two to seven, which enables identification of bound and free entanglement. Through extensive numerical simulations across various quantum state families, we demonstrate that our model achieves high classification accuracy. Furthermore, we leverage the learned representations to generate samples of bound entangled states, the rarest form of entanglement and notoriously difficult to construct analytically. We separately train the same convolutional autoencoders architecture for detecting the presence of quantum discord and show that the model also exhibits high accuracy while requiring significantly less training time. |
| title | Entanglement and discord classification via deep learning |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2601.22253 |