| Description |
Elisa Ercolessi (Bologna)
Abstract:
The recent advances in machine learning algorithms have boosted the application of these techniques to the field of condensed matter physics. Here we demonstrate that, if we employ a training set made by single-particle correlation functions of a non-interacting quantum wire,unsupervised and supervised machine learning techniques are able to reconstruct the phase diagram of a related interacting model and identify topological phases with a high degree of accuracy. |
CMSP Seminar (Joint ICTP/SISSA Statistical Physics): Detection of topological phases of interacting models from single-particle correlation functions via machine learning
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