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| Journal Article | PUBDB-2024-07588 |
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2025
APS
College Park, Md.
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Please use a persistent id in citations: doi:10.1103/PhysRevLett.134.151601 doi:10.3204/PUBDB-2024-07588
Report No.: arXiv:2410.14466
Abstract: 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 $\phi^4$ 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.
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Flow-Based Sampling for Entanglement Entropy and the Machine Learning of Defects
[10.3204/PUBDB-2025-04648]
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