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arXiv · 2512.19891

Efficient Learning of Lattice Gauge Theories with Fermions

Shreya Shukla·Yukari Yamauchi·Andrey Y. Lokhov·Scott Lawrence·Abhijith Jayakumar
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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.

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Shreya Shukla, Yukari Yamauchi, Andrey Y. Lokhov, Scott Lawrence, Abhijith Jayakumar. 2025-12-22. Efficient Learning of Lattice Gauge Theories with Fermions. https://arxiv.org/abs/2512.19891

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