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.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
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.
citing papers explorer
-
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.