{"ID":2826427,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.19891","arxiv_id":"2512.19891","title":"Efficient Learning of Lattice Gauge Theories with Fermions","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.","short_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 famil...","url_abs":"https://arxiv.org/abs/2512.19891","url_pdf":"https://arxiv.org/pdf/2512.19891v1","authors":"[\"Shreya Shukla\",\"Yukari Yamauchi\",\"Andrey Y. Lokhov\",\"Scott Lawrence\",\"Abhijith Jayakumar\"]","published":"2025-12-22T21:34:29Z","proceeding":"hep-lat","tasks":"[\"hep-lat\",\"cs.LG\",\"quant-ph\"]","methods":"[]","has_code":false}