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005     20251217132558.0
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024 7 _ |a arXiv:2410.14466
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024 7 _ |a 10.3204/PUBDB-2025-04648
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037 _ _ |a PUBDB-2025-04648
041 _ _ |a English
082 _ _ |a 530
088 _ _ |a arXiv:2410.14466
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100 1 _ |a Bulgarelli, Andrea
|0 P:(DE-H253)PIP1110088
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|e Corresponding author
245 _ _ |a Flow-Based Sampling for Entanglement Entropy and the Machine Learning of Defects
260 _ _ |c 2025
336 7 _ |a Preprint
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500 _ _ |a Phys. Rev. Lett. 134, 151601 (2025). some discussions improved, matches the published version
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 ϕ4 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.
536 _ _ |a 611 - Fundamental Particles and Forces (POF4-611)
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536 _ _ |a QUEST - QUantum computing for Excellence in Science and Technology (101087126)
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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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536 _ _ |a AQTIVATE - Advanced computing, quantum algorithms, and data-driven approaches for science, technology and engineering (101072344)
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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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856 4 _ |y OpenAccess
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910 1 _ |a Istituto Nazionale di Fisica Nucleare
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910 1 _ |a Deutsches Elektronen-Synchrotron
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910 1 _ |a Deutsches Elektronen-Synchrotron
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910 1 _ |a Deutsches Elektronen-Synchrotron
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913 1 _ |a DE-HGF
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|l Matter and the Universe
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914 1 _ |y 2025
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