Detection of Berezinskii-Kosterlitz-Thouless transition via generative adversarial networks

Year: 2022

Authors: Contessi Daniele; Ricci Elisa; Recati Alessio; Rizzi Matte

Autors Affiliation: Univ Trento, Dipartimento Fis, I-38123 Povo, Italy; INO CNR, BEC Ctr, I-38123 Povo, Italy; Forschungszentrum Julich, Inst Quantum Control, Peter Grunberg Inst PGI 8, D-52425 Julich, Germany; Univ Cologne, Inst Theoret Phys, D-50937 Cologne, Germany; Univ Trento, Dipartimento Ingn & Sci Informaz, I-38123 Povo, Italy; Fdn Bruno Kessler FBK, Deep Visual Learning Res Grp, I-38123 Povo, Italy.

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 ansatze. 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. Copyright D. Contessi et al

Journal/Review: SCIPOST PHYSICS

Volume: 12 (3)      Pages from: 107-1  to: 107-17

More Information: We acknowledge support from the Deutsche Forschungsgemeinschaft (DFG) , project grant 277101999, within the CRC network TR 183 (subproject B01) , the Eu-ropean Union (PASQuanS, Grant No. 817482) , the Alexander von Humboldt Foundation, from Provincia Autonoma di Trento, from Q@TN (the joint lab between University of Trento, FBK-Fondazione Bruno Kessler, INFN-National Institute for Nuclear Physics and CNR-National Research Council) and from the Italian MIUR under the PRIN2017 project CEnTraL. The MPS simulations were run on the JURECA Cluster at the Forschungszentrum Julich, with a code based on a flexible Abelian Symmetric Tensor Networks Library, developed in collaboration with the group of S. Montangero (Padua) .
KeyWords: phase-transitions
DOI: 10.21468/SciPostPhys.12.3.107

Citations: 4
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