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arXiv · 2410.14466

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

Andrea Bulgarelli·Elia Cellini·Karl Jansen·Stefan Kühn·Alessandro Nada·Shinichi Nakajima·Kim A. Nicoli·Marco Panero
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Abstract

We introduce a novel technique to numerically calculate R\'enyi 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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Andrea Bulgarelli, Elia Cellini, Karl Jansen, Stefan Kühn, Alessandro Nada, Shinichi Nakajima, Kim A. Nicoli, Marco Panero. 2024-10-18. Flow-Based Sampling for Entanglement Entropy and the Machine Learning of Defects. https://doi.org/10.1103/physrevlett.134.151601

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