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Machine learning wave functions to identify fractal phases

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Title
Machine learning wave functions to identify fractal phases
Author(s)
Tilen ČadeŽ; Barbara Dietz; Dario Rosa; Alexei Andreanov; Slevin, Keith; Ohtsuki, Tomi
Publication Date
2023-11
Journal
Physical Review B, v.108, no.18
Publisher
American Physical Society
Abstract
We demonstrate that an image recognition algorithm based on a convolutional neural network provides a powerful procedure to differentiate between ergodic, nonergodic extended (fractal), and localized phases in various systems: Single-particle models, including random-matrix and random-graph models, and many-body quantum systems. We propose an efficient procedure in which the network is successfully trained on a small data set of only 500 wave functions (images) per class for a single model which exhibits these phases. The trained network is then used to classify phases in the other models. We discuss the strengths and limitations of the approach. © 2023 American Physical Society.
URI
https://pr.ibs.re.kr/handle/8788114/14754
DOI
10.1103/PhysRevB.108.184202
ISSN
2469-9950
Appears in Collections:
Center for Theoretical Physics of Complex Systems(복잡계 이론물리 연구단) > 1. Journal Papers (저널논문)
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