Description
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.
| Presenter name | Daniele Contessi |
|---|---|
| How will you attend ICAP-27? | I am planning on in-person attendance |
Primary authors
Daniele Contessi
(University of Trento)
Prof.
Elisa Ricci
(University of Trento & Fondazione Bruno Kessler [FBK])
Dr
Alessio Recati
(University of Trento)
Prof.
Matteo Rizzi
(Forschungszentrum Jülich [FZ Jülich] & University of Cologne [UoC])
