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.
Authors / Affiliations: mappings to Contributors and OrganizationsSee all Organizations.
- 1 Forschungszentrum Jülich [FZ Jülich]
- 2 Universität zu Köln / University of Cologne [UoC]
- 3 Università degli Studi di Trento / University of Trento
- 4 Fondazione Bruno Kessler [FBK]
- Alexander von Humboldt-Stiftung / Alexander von Humboldt Foundation
- Deutsche Forschungsgemeinschaft / German Research FoundationDeutsche Forschungsgemeinschaft [DFG]
- Horizon 2020 (through Organization: European Commission [EC])
- Ministero dell’Istruzione, dell’Università e della Ricerca (MIUR) (through Organization: Ministero dell'Istruzione, dell'Università e della Ricerca / Ministry of Education, Universities and Research [MIUR])
- Provincia Autonoma di Trento