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

by D. Contessi, E. Ricci, A. Recati, M. Rizzi

This Submission thread is now published as

Submission summary

As Contributors: Daniele Contessi
Arxiv Link: https://arxiv.org/abs/2110.05383v3 (pdf)
Code repository: https://github.com/cerbero94/GAN_CP
Data repository: https://github.com/cerbero94/GAN_CP/tree/main/data
Date accepted: 2022-02-28
Date submitted: 2022-02-04 19:04
Submitted by: Contessi, Daniele
Submitted to: SciPost Physics
Academic field: Physics
Specialties:
  • Condensed Matter Physics - Computational
  • Quantum Physics
  • Statistical and Soft Matter Physics
Approaches: Theoretical, Computational

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.

Published as SciPost Phys. 12, 107 (2022)



Author comments upon resubmission

We thank the Referees for the remarks.
We directly replied point by point to their comments.

List of changes

The list of all the changes is included in the Author replies to the Referees' reports (see files "Report1.pdf" and "Report2.pdf").

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