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Free keywords:
Condensed Matter, Strongly Correlated Electrons, cond-mat.str-el
Abstract:
We introduce a variational Monte Carlo algorithm for approximating
finite-temperature quantum many-body systems, based on the minimization of a
modified free energy. We employ a variety of trial states -- both tensor
networks as well as neural networks -- as variational ans\"atze for our
numerical optimization. We benchmark and compare different constructions in the
above classes, both for one- and two-dimensional problems, with systems made of
up to \(N=100\) spins. Despite excellent results in one dimension, our results
suggest that the numerical ans\"atze employed have certain expressive
limitations for tackling more challenging two-dimensional systems.
