Autoencoder-based learning of Quantum phase transitions in the two-component Bose-Hubbard model
Year: 2024
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Chowdhury, Iftekher S., Akhouri, Binay Prakash, Haque, Shah, Howard, Eric
Institutions: Macquarie University, Ranchi University
Language: English
Topic: Quantum many-body systems
Subfield: Atomic and Molecular Physics, and Optics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4403798605