Machine learning of quantum phase transitions

TL;DR

This paper introduces a CNN-based machine learning approach combined with quantum Monte Carlo simulations to detect and characterize quantum phase transitions, including intermediate phases, from compressed space-time configurations.

Contribution

It presents a novel method that compresses high-dimensional quantum data for CNN analysis, enabling detection of both continuous and discontinuous phase transitions and identifying unseen intermediate phases.

Findings

Successfully detects continuous quantum phase transitions

Identifies discontinuous quantum phase transitions

Recognizes intermediate phases without prior training

Abstract

Machine learning algorithms provide a new perspective on the study of physical phenomena. In this paper, we explore the nature of quantum phase transitions using multi-color convolutional neural-network (CNN) in combination with quantum Monte Carlo simulations. We propose a method that compresses $d+1$ dimensional space-time configurations to a manageable size and then use them as the input for a CNN. We test our approach on two models and show that both continuous and discontinuous quantum phase transitions can be well detected and characterized. Moreover we show that intermediate phases, which were not trained, can also be identified using our approach.