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001     619369
005     20251119161734.0
024 7 _ |a Bulgarelli:2024yrz
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024 7 _ |a arXiv:2410.14466
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024 7 _ |a 10.1103/PhysRevLett.134.151601
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100 1 _ |a Bulgarelli, Andrea
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245 _ _ |a Flow-based Sampling for Entanglement Entropy and the Machine Learning of Defects
260 _ _ |a College Park, Md.
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500 _ _ |a 10 pages, 9 figures
520 _ _ |a 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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536 _ _ |a DFG project G:(GEPRIS)511713970 - SFB 1639: NuMeriQS: Numerische Methoden zur Untersuchung von Dynamik und Strukturbildung in Quantensystemen (511713970)
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700 1 _ |a Cellini, Elia
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700 1 _ |a Jansen, Karl
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700 1 _ |a Kühn, Stefan
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700 1 _ |a Nada, Alessandro
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700 1 _ |a Nakajima, Shinichi
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700 1 _ |a Nicoli, Kim A.
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700 1 _ |a Panero, Marco
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773 _ _ |a 10.1103/PhysRevLett.134.151601
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787 0 _ |a Bulgarelli, Andrea et.al.
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|t Flow-Based Sampling for Entanglement Entropy and the Machine Learning of Defects
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