TY  - JOUR
AU  - Contessi, Daniele
AU  - Ricci, Elisa
AU  - Recati, Alessio
AU  - Rizzi, Matteo
TI  - Detection of Berezinskii-Kosterlitz-Thouless transition via Generative Adversarial Networks
JO  - SciPost physics
VL  - 12
IS  - 3
SN  - 2542-4653
CY  - Amsterdam
PB  - SciPost Foundation
M1  - FZJ-2022-01924
SP  - 107
PY  - 2022
AB  - 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.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000782238100014
DO  - DOI:10.21468/SciPostPhys.12.3.107
UR  - https://juser.fz-juelich.de/record/907255
ER  -