SKM 2023 – wissenschaftliches Programm

Bereiche | Tage | Auswahl | Suche | Aktualisierungen | Downloads | Hilfe

TT: Fachverband Tiefe Temperaturen

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

Montag, 27. März 2023, 09:30–13:00, HSZ 03

Modern machine learning methods open new perspectives on the high-dimensional data arising naturally in complex quantum systems. The applications range from the analysis of experimental observations over optimal control to the enhancement of numerical simulations in and out of equilibrium. This focus session brings together experts in the field to discuss recent progress and promising directions for future research.
Organizers: Markus Schmitt (University of Cologne), Martin Gärttner (University of Heidelberg)

	09:30	TT 2.1	Hauptvortrag: Enhanced variational Monte Carlo for Rydberg atom arrays — •Stefanie Czischek
	10:00	TT 2.2	Hauptvortrag: Data mining the output of quantum simulators -- from critical behavior to algorithmic complexity — •Marcello Dalmonte
	10:30	TT 2.3	Hauptvortrag: Reinforcement learning for quantum technologies — •Florian Marquardt
	11:00	TT 2.4	Hauptvortrag: Machine learning of phase transition — •Christof Weitenberg
	11:30		15 min. break
	11:45	TT 2.5	Machine learning optimization of Majorana hybrid nanowires — •Matthias Thamm and Bernd Rosenow
	12:00	TT 2.6	Model-independent learning of quantum phases of matter with quantum convolutional neural networks — •Yu-Jie Liu, Adam Smith, Michael Knap, and Frank Pollmann
	12:15	TT 2.7	Simulating spectral functions of two-dimensional systems with neural quantum states — •Tiago Mendes Santos, Markus Schmitt, and Markus Heyl
	12:30	TT 2.8	Efficient optimization of deep neural quantum states toward machine precision — •Ao Chen and Markus Heyl
	12:45	TT 2.9	Time-dependent variational principle for quantum and classical dynamics — •Moritz Reh, Markus Schmitt, and Martin Gärttner