Gauge equivariant neural networks for quantum lattice gauge theories
This work addresses the challenge of efficiently simulating many-body quantum systems with exact local gauge invariance, which is important for physicists studying quantum field theories and quantum materials.
This paper introduces gauge equivariant neural-network quantum states to efficiently simulate many-body quantum systems with exact local gauge invariance. Focusing on the Z2 gauge group on a square lattice, the architecture is shown to contain the loop-gas solution and is used with variational quantum Monte Carlo to describe the ground state wavefunction and demonstrate the confining/deconfining phase transition.
Gauge symmetries play a key role in physics appearing in areas such as quantum field theories of the fundamental particles and emergent degrees of freedom in quantum materials. Motivated by the desire to efficiently simulate many-body quantum systems with exact local gauge invariance, gauge equivariant neural-network quantum states are introduced, which exactly satisfy the local Hilbert space constraints necessary for the description of quantum lattice gauge theory with Zd gauge group on different geometries. Focusing on the special case of Z2 gauge group on a periodically identified square lattice, the equivariant architecture is analytically shown to contain the loop-gas solution as a special case. Gauge equivariant neural-network quantum states are used in combination with variational quantum Monte Carlo to obtain compact descriptions of the ground state wavefunction for the Z2 theory away from the exactly solvable limit, and to demonstrate the confining/deconfining phase transition of the Wilson loop order parameter.