Flow-Based Sampling for Entanglement Entropy and the Machine Learning of Defects
This work addresses a computational bottleneck in quantum field theory for researchers, offering a novel method with demonstrated improvements.
The authors tackled the problem of calculating Rényi entanglement entropies in lattice quantum field theory by introducing a flow-based generative model combined with the replica trick, achieving results that outperform state-of-the-art Monte Carlo methods and show promising scaling with defect size.
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 $φ^4$ 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.