  We propose and analyze a family of approximately-symmetric neural networks
for quantum spin liquid problems. These tailored architectures are
parameter-efficient, scalable, and significantly out-perform existing
symmetry-unaware neural network architectures. Utilizing the mixed-field toric
code model, we demonstrate that our approach is competitive with the
state-of-the-art tensor network and quantum Monte Carlo methods. Moreover, at
the largest system sizes (N=480), our method allows us to explore Hamiltonians
with sign problems beyond the reach of both quantum Monte Carlo and finite-size
matrix-product states. The network comprises an exactly symmetric block
following a non-symmetric block, which we argue learns a transformation of the
ground state analogous to quasiadiabatic continuation. Our work paves the way
toward investigating quantum spin liquid problems within interpretable neural
network architectures
