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 ansätze. We are able to identify gapless-togapped 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.
Detection of Berezinskii-Kosterlitz-Thouless transition via generative adversarial networks / Contessi, D.; Ricci, E.; Recati, A.; Rizzi, M.. - In: SCIPOST PHYSICS. - ISSN 2542-4653. - 12:3(2022). [10.21468/SciPostPhys.12.3.107]
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Titolo: | Detection of Berezinskii-Kosterlitz-Thouless transition via generative adversarial networks | |
Autori: | Contessi, D.; Ricci, E.; Recati, A.; Rizzi, M. | |
Autori Unitn: | ||
Titolo del periodico: | SCIPOST PHYSICS | |
Anno di pubblicazione: | 2022 | |
Numero e parte del fascicolo: | 3 | |
Codice identificativo Scopus: | 2-s2.0-85128181491 | |
Digital Object Identifier (DOI): | http://dx.doi.org/10.21468/SciPostPhys.12.3.107 | |
Handle: | http://hdl.handle.net/11572/341643 | |
Citazione: | Detection of Berezinskii-Kosterlitz-Thouless transition via generative adversarial networks / Contessi, D.; Ricci, E.; Recati, A.; Rizzi, M.. - In: SCIPOST PHYSICS. - ISSN 2542-4653. - 12:3(2022). [10.21468/SciPostPhys.12.3.107] | |
Appare nelle tipologie: | 03.1 Articolo su rivista (Journal article) |