Anna Dawid-Łękowska "Unsupervised machine learning of topological phase transitions from..."
Автор: Centre for AMO Physics at University of Warsaw
Загружено: 2022-01-13
Просмотров: 243
Описание:
Anna Dawid-Łękowska
Institute of Theoretical Physics, Faculty of Physics, University of Warsaw & ICFO, Barcelona, Spain
"Unsupervised machine learning of topological phase transitions from experimental data"
13.01.2022, Optics Seminar, University of Warsaw, Poland
Abstract:
Identifying phase transitions is one of the key challenges in quantum many-body physics. Recently, machine learning methods have been shown to be an alternative way of localising phase boundaries also from noisy and imperfect data and without the knowledge of the order parameter. Here we apply various unsupervised machine learning techniques including anomaly detection and influence functions to experimental data from ultracold atoms. In this way we obtain the topological phase diagram of the Haldane model in a completely unbiased fashion. We show that the methods can successfully be applied to experimental data at finite temperature and to data of Floquet systems, when postprocessing the data to a single micromotion phase. Our work provides a benchmark for unsupervised detection of new exotic phases in complex many-body systems.
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