Using machine learning in the study of quantum phase transition

• Grant number: 21/03074-1
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: August 01, 2021
• End date: July 31, 2023
• Field of knowledge: Physical Sciences and Mathematics - Physics - Condensed Matter Physics
• Principal Investigator: Felipe Fernandes Fanchini
• Grantee: Pedro Marcelo Prado
• Host Institution: Faculdade de Ciências (FC). Universidade Estadual Paulista (UNESP). Campus de Bauru. Bauru , SP, Brazil

Abstract

The study of the quantum phase transitions is an aspect of great interest in the scientific community.Despite the enormous efforts to detect the transitions and classify the phases, this is still a major problem today, given the computational effort required in the diagonalization of high dimension matrices. Our goal, in this sense, is to understand how Machine Learning (ML) techniques can be useful in detecting and classifying quantum phase transitions. We will work with different models, both spin-1 and spin-1/2, where we will test the effectiveness of the ML methods. We intend, using classical ML techniques, to develop a "universal classifier" algorithm, so that given an arbitrary and unknown physical model, it can describe the system's phase diagram. (AU)