Phase Diagram Detection via Gaussian Fitting of Number Probability Distribution

TL;DR

This paper introduces a Gaussian fitting method to analyze the number probability distribution in quantum many-body systems, enabling efficient phase diagram detection that aligns well with advanced techniques and is experimentally accessible.

Contribution

A simple linear fitting protocol is proposed for phase diagram detection in the extended Bose-Hubbard model, offering a practical and accurate alternative to complex methods.

Findings

The method accurately maps the ground-state phase diagram.

Results are comparable to traditional and machine learning techniques.

The approach is experimentally feasible in atomic gases.

Abstract

We investigate the number probability density function that characterizes sub-portions of a quantum many-body system with globally conserved number of particles. We put forward a linear fitting protocol capable of mapping out the ground-state phase diagram of the rich one-dimensional extended Bose-Hubbard model: The results are quantitatively comparable with more sophisticated traditional and machine learning techniques. We argue that the studied quantity should be considered among the most informative bipartite properties, being moreover readily accessible in atomic gases experiments.