TY  - EJOUR
AU  - Bulgarelli, Andrea
AU  - Cellini, Elia
AU  - Jansen, Karl
AU  - Kühn, Stefan
AU  - Nada, Alessandro
AU  - Nakajima, Shinichi
AU  - Nicoli, Kim A.
AU  - Panero, Marco
TI  - Flow-Based Sampling for Entanglement Entropy and the Machine Learning of Defects
IS  - arXiv:2410.14466
M1  - PUBDB-2025-04648
M1  - arXiv:2410.14466
PY  - 2025
N1  - Phys. Rev. Lett. 134, 151601 (2025). some discussions improved, matches the published version
AB  - 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.
LB  - PUB:(DE-HGF)25
DO  - DOI:10.3204/PUBDB-2025-04648
UR  - https://bib-pubdb1.desy.de/record/639736
ER  -