Kavli Affiliate: Eliska Greplova
| First 5 Authors: Thomas Spriggs, Thomas Spriggs, , ,
| Summary:
We present a neural network wavefunction framework for solving non-Abelian
lattice gauge theories in a continuous group representation. Using a
combination of $SU(2)$ equivariant neural networks alongside an $SU(2)$
invariant, physics-inspired ansatz, we learn a parameterization of the ground
state wavefunction of $SU(2)$ lattice gauge theory in 2+1 and 3+1 dimensions.
Our method, performed in the Hamiltonian formulation, has a straightforward
generalization to $SU(N)$. We benchmark our approach against a solely invariant
ansatz by computing the ground state energy, demonstrating the need for bespoke
gauge equivariant transformations. We evaluate the Creutz ratio and average
Wilson loop, and obtain results in strong agreement with perturbative
expansions. Our method opens up an avenue for studying lattice gauge theories
beyond one dimension, with efficient scaling to larger systems, and in a way
that avoids both the sign problem and any discretization of the gauge group.
| Search Query: ArXiv Query: search_query=au:”Eliska Greplova”&id_list=&start=0&max_results=3