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arXiv · 2512.11389

Computing quantum entanglement with machine learning

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

Entanglement calculations in quantum field theories are extremely challenging and typically rely on the replica trick, where the problem is rephrased in a study of defects. We demonstrate that the use of deep generative models drastically outperforms standard Monte Carlo algorithms. Remarkably, such a machine-learning method enables high-precision estimates of R\'enyi entropies in three dimensions for very large lattices. Moreover, we propose a new paradigm for studying lattice defects with flow-based sampling.

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Andrea Bulgarelli, Elia Cellini, Karl Jansen, Stefan Kühn, Alessandro Nada, Shinichi Nakajima, Kim A. Nicoli, Marco Panero. 2025-12-12. Computing quantum entanglement with machine learning. https://arxiv.org/abs/2512.11389

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