Finite temperature properties of strongly correlated systems via variational Monte Carlo

TL;DR

This paper introduces a novel variational Monte Carlo algorithm for calculating finite temperature properties of strongly correlated systems, combining ideas from METTS, VMC, and PIMC, and benchmarks it on the Heisenberg model.

Contribution

The paper develops a new variational finite temperature algorithm (VAFT) that effectively estimates thermal properties in higher-dimensional systems.

Findings

Successfully benchmarks VAFT against exact results for the Heisenberg model.

Demonstrates the potential of VAFT for finite temperature studies of strongly correlated materials.

Bridges the gap between ground state variational methods and finite temperature calculations.

Abstract

Variational methods are a common approach for computing properties of ground states but have not yet found analogous success in finite temperature calculations. In this work we develop a new variational finite temperature algorithm (VAFT) which combines ideas from minimally entangled typical thermal states (METTS), variational Monte Carlo (VMC) optimization and path integral Monte Carlo (PIMC). This allows us to define an implicit variational density matrix to estimate finite temperature properties in two and three dimensions. We benchmark the algorithm on the bipartite Heisenberg model and compare to exact results.