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000619369 245__ $$aFlow-based Sampling for Entanglement Entropy and the Machine Learning of Defects
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000619369 520__ $$aWe 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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000619369 7001_ $$0P:(DE-H253)PIP1003636$$aJansen, Karl$$b2
000619369 7001_ $$0P:(DE-H253)PIP1086314$$aKühn, Stefan$$b3$$eCorresponding author
000619369 7001_ $$0P:(DE-HGF)0$$aNada, Alessandro$$b4
000619369 7001_ $$0P:(DE-HGF)0$$aNakajima, Shinichi$$b5
000619369 7001_ $$0P:(DE-HGF)0$$aNicoli, Kim A.$$b6
000619369 7001_ $$0P:(DE-HGF)0$$aPanero, Marco$$b7
000619369 773__ $$0PERI:(DE-600)1472655-5$$a10.1103/PhysRevLett.134.151601$$gVol. 134, no. 15, p. 151601$$n15$$p151601$$tPhysical review letters$$v134$$x0031-9007$$y2025
000619369 7870_ $$0PUBDB-2025-04648$$aBulgarelli, Andrea et.al.$$d2025$$iIsParent$$rarXiv:2410.14466$$tFlow-Based Sampling for Entanglement Entropy and the Machine Learning of Defects
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