Universität zu Köln

Contessi, Daniele, Ricci, Elisa, Recati, Alessio and Rizzi, Matteo (2022). Detection of Berezinskii-Kosterlitz-Thouless transition via generative adversarial networks. SciPost Phys., 12 (3). AMSTERDAM: SCIPOST FOUNDATION. ISSN 2542-4653

Full text not available from this repository.

Abstract

The detection of phase transitions in quantum many-body systems with lowest possible prior knowledge of their details is among the most rousing goals of the flourishing application of machine-learning techniques to physical questions. Here, we train a Generative Adversarial Network (GAN) with the Entanglement Spectrum of a system bipartition, as extracted by means of Matrix Product States ansatze. We are able to identify gapless-to-gapped phase transitions in different one-dimensional models by looking at the machine inability to reconstruct outsider data with respect to the training set. We foresee that GAN-based methods will become instrumental in anomaly detection schemes applied to the determination of phase-diagrams. Copyright D. Contessi et al.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Contessi, Daniele UNSPECIFIED UNSPECIFIED UNSPECIFIED Ricci, Elisa UNSPECIFIED UNSPECIFIED UNSPECIFIED Recati, Alessio UNSPECIFIED UNSPECIFIED UNSPECIFIED Rizzi, Matteo UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-685513
DOI:	10.21468/SciPostPhys.12.3.107
Journal or Publication Title:	SciPost Phys.
Volume:	12
Number:	3
Date:	2022
Publisher:	SCIPOST FOUNDATION
Place of Publication:	AMSTERDAM
ISSN:	2542-4653
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language PHASE-TRANSITIONS Multiple languages Physics, Multidisciplinary Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/68551