Dynamical Variational Monte Carlo as a quantum impurity solver: Application to Cluster Dynamical Mean-Field Theory

TL;DR

This paper introduces a dynamical variational Monte Carlo impurity solver for cluster dynamical mean-field theory, capable of handling larger systems efficiently and improving the accuracy of strongly-correlated electron system simulations.

Contribution

The paper develops and benchmarks a new impurity solver based on dynamical variational Monte Carlo, extending the system sizes accessible in CDMFT beyond current methods.

Findings

Successfully benchmarks the solver against exact diagonalization for small clusters.

Achieves results consistent with the exact solution in the thermodynamic limit.

Demonstrates the solver's potential for studying larger, more complex systems.

Abstract

Two of the primary sources of error in the Cluster dynamical mean-field theory (CDMFT) technique arise from the use of finite size clusters and finite size baths, which makes the development of impurity solvers that can treat larger systems an essential goal. In this work we introduce an impurity solver based on the recently developed dynamical variational Monte Carlo (dVMC) method. Variational Monte Carlo possesses a favorable scaling as a function of system size, which enables the treatment of systems beyond the reach of current exact diagonalization solvers. To benchmark the technique, we perform a systematic set of CDMFT calculations on the one-dimensional Hubbard model. We compare to results obtained with an exact diagonalization solver for small clusters, and against the exact solution in the thermodynamic limit obtained by Lieb and Wu for larger clusters. The development of improved impurity solvers will help extend the reach of quantum cluster methods, which can be applied to a wide range of strongly-correlated electron systems, promising new insights on their emergent behavior.