%0 Electronic Article
%A Bulgarelli, Andrea
%A Cellini, Elia
%A Jansen, Karl
%A Kühn, Stefan
%A Nada, Alessandro
%A Nakajima, Shinichi
%A Nicoli, Kim A.
%A Panero, Marco
%T Flow-Based Sampling for Entanglement Entropy and the Machine Learning of Defects
%N arXiv:2410.14466
%M PUBDB-2025-04648
%M arXiv:2410.14466
%D 2025
%Z Phys. Rev. Lett. 134, 151601 (2025). some discussions improved, matches the published version
%X 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.
%F PUB:(DE-HGF)25
%9 Preprint
%R 10.3204/PUBDB-2025-04648
%U https://bib-pubdb1.desy.de/record/639736