Tensor network investigation of the hard-square model
Samuel Nyckees, Fr\'ed\'eric Mila

TL;DR
This study employs tensor network methods to analyze phase transitions in the hard-square model, revealing new phase boundaries and confirming universality classes, with results relevant for classical and quantum analogs.
Contribution
It demonstrates the effectiveness of tensor networks in studying the hard-square model and uncovers previously unreported phase transitions and detailed phase diagrams.
Findings
Confirmed the transition near the 3-state Potts point belongs to the Huse-Fisher class.
Identified a Lifshitz disorder line and an Ising transition at high activity.
Found the phase diagram matches the 1D quantum Rydberg atom model, with a tricritical Ising point.
Abstract
Using the corner-transfer matrix renormalization group to contract the tensor network that describes its partition function, we investigate the nature of the phase transitions of the hard-square model, one of the exactly solved models of statistical physics for which Baxter has found an integrable manifold. The motivation is twofold: assess the power of tensor networks for such models, and probe the 2D classical analog of a 1D quantum model of hard-core bosons that has recently attracted significant attention in the context of experiments on chains of Rydberg atoms. Accordingly, we concentrate on two planes in the 3D parameter space spanned by the activity and the coupling constants in the two diagonal directions. We first investigate the only case studied so far with Monte Carlo simulations, the case of opposite coupling constants. We confirm that, away and not too far from the…
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Taxonomy
TopicsQuantum many-body systems · Theoretical and Computational Physics · Complex Network Analysis Techniques
