Flow-Based Sampling for Entanglement Entropy and the Machine Learning of Defects
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866909580882083840 |
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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 | We introduce a novel technique to numerically calculate Rényi entanglement entropies in lattice quantum field theory using generative models. We describe how flow-based approaches can be combined with the replica trick using a custom neural-network architecture around a lattice defect connecting two replicas. Numerical tests for the $ϕ^4$ scalar field theory in two and three dimensions demonstrate that our technique outperforms state-of-the-art Monte Carlo calculations, and exhibit a promising scaling with the defect size. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_14466 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Flow-Based Sampling for Entanglement Entropy and the Machine Learning of Defects Bulgarelli, Andrea Cellini, Elia Jansen, Karl Kühn, Stefan Nada, Alessandro Nakajima, Shinichi Nicoli, Kim A. Panero, Marco Quantum Physics Statistical Mechanics Machine Learning High Energy Physics - Lattice We introduce a novel technique to numerically calculate Rényi entanglement entropies in lattice quantum field theory using generative models. We describe how flow-based approaches can be combined with the replica trick using a custom neural-network architecture around a lattice defect connecting two replicas. Numerical tests for the $ϕ^4$ scalar field theory in two and three dimensions demonstrate that our technique outperforms state-of-the-art Monte Carlo calculations, and exhibit a promising scaling with the defect size. |
| title | Flow-Based Sampling for Entanglement Entropy and the Machine Learning of Defects |
| topic | Quantum Physics Statistical Mechanics Machine Learning High Energy Physics - Lattice |
| url | https://arxiv.org/abs/2410.14466 |