## UC Berkeley / Lawrence Berkeley Laboratory

#### First-quantized neural networks for lattice fermions

**James Stokes, Flatiron Institute**

First-quantized deep neural network techniques are developed
for analyzing strongly coupled fermionic systems on the lattice. Using
a Slater-Jastrow inspired ansatz which exploits deep residual networks
with convolutional residual blocks, we approximately determine the
ground state of spinless fermions on a square lattice with
nearest-neighbor interactions. The flexibility of the neural-network
ansatz results in a high level of accuracy when compared to exact
diagonalization results on small systems, both for energy and
correlation functions. On large systems, we obtain accurate estimates
of the boundaries between metallic and charge ordered phases as a
function of the interaction strength and the particle density.