Phases of two-dimensional spinless lattice fermions with first-quantized deep neural-network quantum states

Understand

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.