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Detection of Berezinskii-Kosterlitz-Thouless transition via Generative Adversarial Networks
by D. Contessi, E. Ricci, A. Recati, M. Rizzi
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Submission summary
| Authors (as registered SciPost users): | Daniele Contessi |
| Submission information | |
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| Preprint 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 |
| Ontological classification | |
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| Academic field: | Physics |
| Specialties: |
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| 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.
Author comments upon resubmission
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").
Published as SciPost Phys. 12, 107 (2022)