Flow-Based Sampling for Entanglement Entropy and the Machine Learning of Defects

Abstract

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 ${\varphi }^4$ 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.

some discussions improved, matches the published version

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Cited by in corpus (3)

• Scaling of Stochastic Normalizing Flows in $\mathrm{SU}(3)$ lattice gauge theory
• Scaling flow-based approaches for topology sampling in $\mathrm{SU}(3)$ gauge theory
• Casimir effect in critical $\mathrm{O}(N)$ models from non-equilibrium Monte Carlo simulations