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), pp. 10701-10717. [10.21468/SciPostPhys.12.3.107]

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

Contessi D.;Ricci E.;Recati A.;
2022-01-01

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 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.
2022
3
Contessi, D.; Ricci, E.; Recati, A.; Rizzi, M.
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), pp. 10701-10717. [10.21468/SciPostPhys.12.3.107]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/341643
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