Phases of two-dimensional spinless lattice fermions with first-quantized deep neural-network quantum states
This work addresses the challenge of simulating quantum many-body systems for condensed matter physics, representing an incremental improvement with a novel method for a known bottleneck.
The authors tackled the problem of analyzing strongly coupled fermionic systems on a lattice by developing first-quantized deep neural network techniques, achieving high accuracy in ground state energy and correlation functions compared to exact diagonalization on small systems and accurately estimating phase boundaries between metallic and charge ordered phases on large systems.
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.