Visual configuration segmentation of quantum states for phase identification in many-body systems

TL;DR

This paper introduces a novel visual configuration segmentation method that uses AI and computer vision to identify quantum phases and phase transitions in many-body systems without prior knowledge of order parameters.

Contribution

The paper presents a new segmentation scheme called visual configuration segmentation (VCS) that visualizes quantum states and detects phase transitions in strongly correlated systems.

Findings

Successfully visualized quantum phases using VCS

Identified critical points without prior phase knowledge

Demonstrated effectiveness on various spin systems

Abstract

Artificial intelligence provides an unprecedented perspective for studying phases of matter in condensed-matter systems. Image segmentation is a basic technique of computer vision that belongs to a branch of artificial intelligence. In this work, we propose a segmentation scheme named visual configuration segmentation (VCS) to unveil quantum phases and quantum phase transitions in many-body systems. By encoding the information of renormalized quantum states into a color image and segmenting the color image through the VCS, the renormalized quantum states can be visualized, from which quantum phase transitions can be revealed and the corresponding critical points can be identified. Our scheme is benchmarked on several strongly correlated spin systems, which does not depend on the priori knowledge of order parameters of quantum phases. This demonstrates the potential to disclose the underlying structure of quantum phases by the techniques of computer vision.