Gauge-Equivariant Graph Neural Networks for Lattice Gauge Theories
Local gauge symmetry underlies fundamental interactions and strongly correlated quantum matter, yet existing machine-learning approaches lack a general, principled framework for learning under site-dependent symmetries, particularly for intrinsically nonlocal observables. Here we introduce a gauge-equivariant graph neural network that embeds non-Abelian symmetry directly into message passing via matrix-valued, gauge-covariant features and symmetry-compatible updates, extending equivariant learning from global to fully local symmetries. In this formulation, message passing implements gauge-covariant transport across the lattice, allowing nonlocal correlations and loop-like structures to em...
| Authors | Ali Rayat, Yaohang Li, Gia-Wei Chern |
|---|---|
| Primary category | cond-mat.str-el |
| Published | 22 April 2026 |
| Last updated at source | 22 April 2026 |
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