Autoencoder-based learning of Quantum phase transitions in the two-component Bose-Hubbard model

TL;DR

This paper explores how autoencoders and machine learning techniques can effectively detect and analyze quantum phase transitions in the two-component Bose-Hubbard model, providing insights into phase boundaries and critical points.

Contribution

It introduces the application of autoencoders to quantum phase transition detection, highlighting their ability to analyze latent space and identify phase boundaries in the Bose-Hubbard model.

Findings

Autoencoders successfully identify phase boundaries.

Latent space representations reveal critical points.

Dimensionality reduction aids visualization of quantum phases.

Abstract

This paper investigates the use of autoencoders and machine learning methods for detecting and analyzing quantum phase transitions in the Two-Component Bose-Hubbard Model. By leveraging deep learning models such as autoencoders, we investigate latent space representations, reconstruction error analysis, and cluster distance calculations to identify phase boundaries and critical points. The study is supplemented by dimensionality reduction techniques such as PCA and t-SNE for latent space visualization. The results demonstrate the potential of autoencoders to describe the dynamics of quantum phase transitions.