Related papers: The classical two-dimensional Heisenberg model revisited: An $SU(2)$-symmetric tensor network study

The classical two-dimensional Heisenberg model revisited: An $SU(2)$-symmetric tensor network study

URL: http://arxiv.org/abs/2106.06310v2
Date: Fri, 25 Jun 2021 14:44:44 GMT
Title: The classical two-dimensional Heisenberg model revisited: An $SU(2)$-symmetric tensor network study
Authors: Philipp Schmoll, Augustine Kshetrimayum, Jens Eisert, Roman Orus, Matteo Rizzi
Abstract summary: We make use of state-the-art tensor network approaches to explore the correlation structure for Gibbs states. We find a rapidly diverging correlation length, whose behaviour is apparently compatible with two main contradictory hypotheses.
Score: 0.6299766708197883
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: The classical Heisenberg model in two spatial dimensions constitutes one of the most paradigmatic spin models, taking an important role in statistical and condensed matter physics to understand magnetism. Still, despite its paradigmatic character and the widely accepted ban of a (continuous) spontaneous symmetry breaking, controversies remain whether the model exhibits a phase transition at finite temperature. Importantly, the model can be interpreted as a lattice discretization of the $O(3)$ non-linear sigma model in $1+1$ dimensions, one of the simplest quantum field theories encompassing crucial features of celebrated higher-dimensional ones (like quantum chromodynamics in $3+1$ dimensions), namely the phenomenon of asymptotic freedom. This should also exclude finite-temperature transitions, but lattice effects might play a significant role in correcting the mainstream picture. In this work, we make use of state-of-the-art tensor network approaches, representing the classical partition function in the thermodynamic limit over a large range of temperatures, to comprehensively explore the correlation structure for Gibbs states. By implementing an $SU(2)$ symmetry in our two-dimensional tensor network contraction scheme, we are able to handle very large effective bond dimensions of the environment up to $\chi_E^\text{eff} \sim 1500$, a feature that is crucial in detecting phase transitions. With decreasing temperatures, we find a rapidly diverging correlation length, whose behaviour is apparently compatible with the two main contradictory hypotheses known in the literature, namely a finite-$T$ transition and asymptotic freedom, though with a slight preference for the second.

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