  Neural networks are a promising tool for simulating quantum many body
systems. Recently, it has been shown that neural network-based models describe
quantum many body systems more accurately when they are constrained to have the
correct symmetry properties. In this paper, we show how to create maximally
expressive models for quantum states with specific symmetry properties by
drawing on literature from the machine learning community. We implement group
equivariant convolutional networks (G-CNN) \cite{cohen2016group}, and
demonstrate that performance improvements can be achieved without increasing
memory use. We show that G-CNNs achieve very good accuracy for Heisenberg
quantum spin models in both ordered and spin liquid regimes, and improve the
ground state accuracy on the triangular lattice over other variational
Monte-Carlo methods.
