Finite temperature properties of strongly correlated systems via variational Monte Carlo

摘要

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