Journal Article

FZJ-2022-01924

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Detection of Berezinskii-Kosterlitz-Thouless transition via Generative Adversarial Networks

Contessi, D. (Corresponding author)FZJ* ; Ricci, E. ; Recati, A. ; Rizzi, M.FZJ*

2022
SciPost Foundation Amsterdam

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Please use a persistent id in citations: http://hdl.handle.net/2128/31040  doi:10.21468/SciPostPhys.12.3.107

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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Contributing Institute(s):

1. Quantum Control (PGI-8)

Research Program(s):

1. 5221 - Advanced Solid-State Qubits and Qubit Systems (POF4-522) (POF4-522)
2. PASQuanS - Programmable Atomic Large-Scale Quantum Simulation (817482) (817482)

Appears in the scientific report 2022

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Database coverage:
; ; ; ; Clarivate Analytics Master Journal List ; Current Contents - Physical, Chemical and Earth Sciences ; DOAJ Seal ; Essential Science Indicators ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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