SAMOP 2023 – wissenschaftliches Programm

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SYML: Symposium Machine Learning in Atomic and Molecular Physics

SYML 1: Machine Learning in Atomic and Molecular Physics

Dienstag, 7. März 2023, 11:00–13:00, E415

Machine-learning tools are increasingly employed to assist challenging problems in natural sciences. In atomic and molecular physics this notably includes the solution of the electronic Schrödinger equation, efficient quantum state tomography, problems in quantum computing and quantum simulation, optimal control of atomic systems, and inverse problems in x-ray-diffraction imaging and spectroscopy. This symposium gathers experts from experiments and theory and aims to provide an overview of this rapidly growing topic.

11:00 SYML 1.1 Hauptvortrag: An unsupervised deep learning algorithm for single-site reconstruction in quantum gas microscopes — •Alexander Impertro, Julian F. Wienand, Sophie Häfele, Hendrik von Raven, Scott Hubele, Till Klostermann, Cesar R. Cabrera, Immanuel Bloch, and Monika Aidelsburger
11:30 SYML 1.2 Hauptvortrag: Physics-inspired learning algorithms for optimal shaping of atoms with light — •Maximilian Prüfer
12:00 SYML 1.3 Hauptvortrag: Machine-Learning assisted quantum computing and interferometry — •Ludwig Mathey, Lukas Broers, and Nicolas Heimann
12:30 SYML 1.4 Hauptvortrag: Efficient quantum state tomography with convolutional neural networks — •Moritz Reh, Tobias Schmale, and Martin Gärttner
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