Finite temperature properties of strongly correlated systems via variational Monte Carlo

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.