Gauge-equivariant graph neural networks embed non-Abelian local symmetries directly into message passing for lattice gauge theories, enabling learning of nonlocal observables from local operations.
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A gauge-invariant GNN using Wilson loops as inputs accurately predicts observables and simulates dynamics in Z2 and U(1) lattice gauge models.
An unbiased time-dependent variational Monte Carlo method is introduced via self-normalized importance sampling on a cutoff-deformed Born distribution, with a complementary tensor cross interpolation approach explored.
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Gauge-Equivariant Graph Neural Networks for Lattice Gauge Theories
Gauge-equivariant graph neural networks embed non-Abelian local symmetries directly into message passing for lattice gauge theories, enabling learning of nonlocal observables from local operations.
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Graph Neural Networks in the Wilson Loop Representation of Abelian Lattice Gauge Theories
A gauge-invariant GNN using Wilson loops as inputs accurately predicts observables and simulates dynamics in Z2 and U(1) lattice gauge models.
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Time-dependent variational Monte Carlo without bias
An unbiased time-dependent variational Monte Carlo method is introduced via self-normalized importance sampling on a cutoff-deformed Born distribution, with a complementary tensor cross interpolation approach explored.