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Detection of Berezinskii-Kosterlitz-Thouless transition via Generative Adversarial Networks

Daniele Contessi, Elisa Ricci, Alessio Recati, Matteo Rizzi

SciPost Phys. 12, 107 (2022) · published 25 March 2022

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 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.


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