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• W4229045900 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-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." @default.
• W4229045900 created "2022-05-08" @default.
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• W4229045900 date "2022-03-25" @default.
• W4229045900 modified "2023-09-30" @default.
• W4229045900 title "Detection of Berezinskii-Kosterlitz-Thouless transition via Generative Adversarial Networks" @default.
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• W4229045900 doi "https://doi.org/10.21468/scipostphys.12.3.107" @default.
• W4229045900 hasPublicationYear "2022" @default.
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