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

• URL: http://arxiv.org/abs/2110.05383v3
• Date: Wed, 2 Feb 2022 07:42:15 GMT
• Title: Detection of Berezinskii-Kosterlitz-Thouless transition via Generative Adversarial Networks
• Authors: D. Contessi, E. Ricci, A. Recati, M. Rizzi
• Abstract summary: We train a Geneversarative Adrial Network (GAN) with the Entanglement Spectrum of a system bipartition. We are able to identify gapless-to-gapped phase transitions in different one-dimensional models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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