  Deep neural networks have been shown as a potentially powerful ansatz in
variational Monte Carlo for solving quantum many-body problems. We propose two
improvements in this direction. The first is graph neural ansatz (GNA), which
is a variational wavefunction universal to arbitrary geometry. GNA results in
accurate ground-state energies on 2D Kagome lattices, triangular lattices, and
randomly connected graphs. Secondly, we design a distributed workflow on
multiple accelerators to scale up the computation. We compute Kagome lattices
with sizes up to 432 sites on 128 TPU cores. The parameter sharing nature of
the GNA also leads to transferability across different system sizes and
geometries.
