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Quantum Simulation
Efficient Learning of Lattice Gauge Theories with Fermions
arXiv
Authors: Shreya Shukla, Yukari Yamauchi, Andrey Y. Lokhov, Scott Lawrence, Abhijith Jayakumar
Year
2025
Paper ID
36360
Status
Preprint
Abstract Read
~2 min
Abstract Words
87
Citations
N/A
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.
Why This Paper Matters
- This paper contributes to the Quantum Simulation research area in the Quantum Articles archive.
- It adds a 2025 reference point for readers tracking recent quantum research.
- We introduce a learning method for recovering action parameters in lattice field theories.
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