Abstract
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 from noisy and imperfect data without the knowledge of the order parameter. Here, we apply different 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 these methods can successfully be applied to experimental data at finite temperatures and to the data of Floquet systems when post-processing the data to a single micromotion phase. Our work provides a benchmark for the unsupervised detection of new exotic phases in complex many-body systems.
Funder
Ministerio de Economía y Competitividad
Fundacja na rzecz Nauki Polskiej
Narodowe Centrum Nauki
Fundación Cellex
European Social Fund
“la Caixa” Foundation
Deutsche Forschungsgemeinschaft
H2020 European Research Council
European Regional Development Fund
Spanish Ministry of Economy and Competitiveness
Fundació Mir-Puig
Generalitat de Catalunya
Agència de Gestió d’Ajuts Universitaris i de Recerca
Subject
Artificial Intelligence,Human-Computer Interaction,Software
Cited by
41 articles.
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