Re-entrance and entanglement in the one-dimensional Bose-Hubbard model

TL;DR

This paper investigates the re-entrant phase transition in the 1D Bose-Hubbard model using infinite matrix product states, revealing entanglement thresholds and accurately estimating the critical point of the transition.

Contribution

It introduces a finite-entanglement scaling method to analyze the Kosterlitz-Thouless transition in the Bose-Hubbard model, providing both qualitative and quantitative insights.

Findings

Re-entrance appears only when matrix size chi >= 8.

Entanglement threshold coincides with capturing particle-hole excitations.

Estimated KT transition point at t_KT = 0.30 +/- 0.01.

Abstract

Re-entrance is a novel feature where the phase boundaries of a system exhibit a succession of transitions between two phases A and B, like A-B-A-B, when just one parameter is varied monotonically. This type of re-entrance is displayed by the 1D Bose Hubbard model between its Mott insulator (MI) and superfluid phase as the hopping amplitude is increased from zero. Here we analyse this counter-intuitive phenomenon directly in the thermodynamic limit by utilizing the infinite time-evolving block decimation algorithm to variationally minimize an infinite matrix product state (MPS) parameterized by a matrix size chi. Exploiting the direct restriction on the half-chain entanglement imposed by fixing chi, we determined that re-entrance in the MI lobes only emerges in this approximate when chi >= 8. This entanglement threshold is found to be coincident with the ability an infinite MPS to be simultaneously particle-number symmetric and capture the kinetic energy carried by particle-hole excitations above the MI. Focussing on the tip of the MI lobe we then applied, for the first time, a general finite-entanglement scaling analysis of the infinite order Kosterlitz-Thouless critical point located there. By analysing chi's up to a very moderate chi = 70 we obtained an estimate of the KT transition as t_KT = 0.30 +/- 0.01, demonstrating the how a finite-entanglement approach can provide not only qualitative insight but also quantitatively accurate predictions.