Computing quantum entanglement with machine learning
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911315751075840 |
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| author | Bulgarelli, Andrea Cellini, Elia Jansen, Karl Kühn, Stefan Nada, Alessandro Nakajima, Shinichi Nicoli, Kim A. Panero, Marco |
| author_facet | Bulgarelli, Andrea Cellini, Elia Jansen, Karl Kühn, Stefan Nada, Alessandro Nakajima, Shinichi Nicoli, Kim A. Panero, Marco |
| contents | Entanglement calculations in quantum field theories are extremely challenging and typically rely on the replica trick, where the problem is rephrased in a study of defects. We demonstrate that the use of deep generative models drastically outperforms standard Monte Carlo algorithms. Remarkably, such a machine-learning method enables high-precision estimates of Rényi entropies in three dimensions for very large lattices. Moreover, we propose a new paradigm for studying lattice defects with flow-based sampling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_11389 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Computing quantum entanglement with machine learning Bulgarelli, Andrea Cellini, Elia Jansen, Karl Kühn, Stefan Nada, Alessandro Nakajima, Shinichi Nicoli, Kim A. Panero, Marco High Energy Physics - Lattice Entanglement calculations in quantum field theories are extremely challenging and typically rely on the replica trick, where the problem is rephrased in a study of defects. We demonstrate that the use of deep generative models drastically outperforms standard Monte Carlo algorithms. Remarkably, such a machine-learning method enables high-precision estimates of Rényi entropies in three dimensions for very large lattices. Moreover, we propose a new paradigm for studying lattice defects with flow-based sampling. |
| title | Computing quantum entanglement with machine learning |
| topic | High Energy Physics - Lattice |
| url | https://arxiv.org/abs/2512.11389 |