SKM 2023 – wissenschaftliches Programm

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TT: Fachverband Tiefe Temperaturen

TT 2: Focus Session: Physics Meets ML I – Machine Learning for Complex Quantum Systems (joint session TT/DY)

TT 2.4: Hauptvortrag

Montag, 27. März 2023, 11:00–11:30, HSZ 03

Machine learning of phase transition — •Christof Weitenberg — Universität Hamburg, Institut für Laserphysik, Hamburg, Germany

Machine learning is emerging as vital tool in many sciences. In quantum physics, notable examples are neural networks for the efficient representation of quantum many-body states and reinforcement learning of preparation and read-out routines. In this talk, I will present our results on machine learning of quantum phase transitions using classification techniques. This approach works very well even on noisy experimental data both with supervised and unsupervised machine learning, as we demonstrate for quantum simulators based on ultracold atoms. Next to the practical advantages, such techniques might in the future reveal phase transitions, for which conventional order parameters are not known.