%0 Journal Article
%A Contessi, Daniele
%A Ricci, Elisa
%A Recati, Alessio
%A Rizzi, Matteo
%T Detection of Berezinskii-Kosterlitz-Thouless transition via Generative Adversarial Networks
%J SciPost physics
%V 12
%N 3
%@ 2542-4653
%C Amsterdam
%I SciPost Foundation
%M FZJ-2022-01924
%P 107
%D 2022
%X 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 ans\"atze. 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.
%F PUB:(DE-HGF)16
%9 Journal Article
%U <Go to ISI:>//WOS:000782238100014
%R 10.21468/SciPostPhys.12.3.107
%U https://juser.fz-juelich.de/record/907255