Efficient Learning of Lattice Gauge Theories with Fermions
High Energy Physics - Lattice
2025-12-24 v1 Machine Learning
Quantum Physics
Abstract
We introduce a learning method for recovering action parameters in lattice field theories. Our method is based on the minimization of a convex loss function constructed using the Schwinger-Dyson relations. We show that score matching, a popular learning method, is a special case of our construction of an infinite family of valid loss functions. Importantly, our general Schwinger-Dyson-based construction applies to gauge theories and models with Grassmann-valued fields used to represent dynamical fermions. In particular, we extend our method to realistic lattice field theories including quantum chromodynamics.
Keywords
Cite
@article{arxiv.2512.19891,
title = {Efficient Learning of Lattice Gauge Theories with Fermions},
author = {Shreya Shukla and Yukari Yamauchi and Andrey Y. Lokhov and Scott Lawrence and Abhijith Jayakumar},
journal= {arXiv preprint arXiv:2512.19891},
year = {2025}
}
Comments
12 pages, 2 figures