Freiburg 2024 – wissenschaftliches Programm
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Q: Fachverband Quantenoptik und Photonik
Q 24: Poster II
Q 24.28: Poster
Dienstag, 12. März 2024, 17:00–19:00, KG I Foyer
Identification of Quantum Phases with Unsupervised Machine Learning — •Niklas Käming1,3, Paolo Stornati2, Klaus Sengstock1,3,4, and Christof Weitenberg1,3,4 — 1IQP - Institut für Quantenphysik, Universität Hamburg, Luruper Chaussee 149, 22761 Hamburg, Germany — 2ICFO - Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, Av. Carl Friedrich Gauss 3, 08860 Castelldefels (Barcelona), Spain — 3The Hamburg Centre for Ultrafast Imaging, Luruper Chaussee 149, 22761 Hamburg, Germany — 4ZOQ - Zentrum für Optische Quantentechnologien, Universität Hamburg, Luruper Chaussee 149, 22761 Hamburg, Germany
Machine learning techniques are a versatile tool to identify many-body quantum states without knowledge of the order parameters. Using such techniques to identify phases of matter has gained high popularity in many-body physics and the cold quantum gas community. In this poster, we present unsupervised machine-learning techniques that have been proven to be universally successful in mapping out the extended Fermi-Hubbard model from simulated entanglement spectra and the Haldane model from experimental cold quantum gas data. In the future, we hope to find new phases of matter by performing experiments in theoretical non-tractable regimes.
Keywords: Fermi Hubbard Model; Machine Learning; Phase Transitions