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

Autor:	Chowdhury, Iftekher S., Akhouri, Binay Prakash, Haque, Shah, Howard, Eric
Rok vydání:	2024
Předmět:	Condensed Matter - Quantum Gases High Energy Physics - Lattice Quantum Physics
Druh dokumentu:	Working Paper
Popis:	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. Comment: 24 pages, 7 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2409.18480 Zobrazit plný text záznamu View this record from Arxiv