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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GCNN variational states optimized with directed-loop sampling yield a 4-fold degenerate ground state for V ≤ 0.4 in the quantum dimer model, with benchmarks matching ED and QMC up to L=32.
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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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Group Convolutional Neural Network for the Low-Energy Spectrum in the Quantum Dimer Model
GCNN variational states optimized with directed-loop sampling yield a 4-fold degenerate ground state for V ≤ 0.4 in the quantum dimer model, with benchmarks matching ED and QMC up to L=32.