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TT: Fachverband Tiefe Temperaturen

TT 2: Focus Session: Artificial Intelligence in Condensed Matter Physics I (joint session TT/DY)

Montag, 18. März 2024, 09:30–13:15, H 0104

While artificial intelligence leaves an ever growing footprint in our everyday lives, it has as well inspired various new approaches in the physical sciences; for instance, one of the outstanding success stories is the prediction of protein folding with unprecedented accuracy. But what role can AI play in condensed matter physics? This symposium aims to provide an overview and discussion of recent applications of modern machine learning and its prospects for the advancement of research in this field. The increasingly data-intensive experiments with high-dimensional observations call for the development of new tools for analysis matching known strengths of machine learning algorithms. Reinforcement learning agents can be employed to precisely manipulate many-body systems, which, among other use cases, is a pivotal ingredient for quantum technologies. On the computational side, ideas from deep learning and generative modeling inspire new building blocks to boost numerical simulations. One may even ask the question whether a machine can autonomously discover physical concepts such as effective degrees of freedom or equations of motion, and reveal them in an interpretable manner to human researchers.
Please note the second part of this session which will take place this afternoon, TT 14 (15:00 − 16:00) in the lecture Hall H3025.
Prof. Dr. Simon Trebst, Universität Köln
Prof. Dr. Florian Marquardt, Max-Planck-Institut Erlangen
Dr. Markus Schmitt, FZ Jülich

	09:30	TT 2.1	Hauptvortrag: Exploring artificial intelligence for engineered quantum matter — •Eliska Greplova
	10:00	TT 2.2	Hauptvortrag: Communicability as a criterion for interpretable representations — •Renato Renner
	10:30	TT 2.3	Hauptvortrag: Disentangling Multiqubit States using Deep Reinforcement Learning — •Marin Bukov
	11:00		15 min. break
	11:15	TT 2.4	Hauptvortrag: Neural Quantum States For The Many-Electron Problem — •Giuseppe Carleo
	11:45	TT 2.5	Hauptvortrag: Neural quantum states for strongly correlated systems: learning from data and Hamiltonians — •Annabelle Bohrdt, Hannah Lange, Schuyler Moss, Fabian Döschl, Felix Palm, Giulia Semeghini, Mikhail Lukin, Sepehr Ebadi, Tout Wang, Fabian Grusdt, Juan Carrasquilla, and Roger Melko
	12:15	TT 2.6	Hauptvortrag: Towards an Artificial Muse for new Ideas in Quantum Physics — •Mario Krenn
	12:45	TT 2.7	Adversarial Hamiltonian learning of quantum dots in a minimal Kitaev chain — •Rouven Koch, David van Driel, Alberto Bordin, Jose L. Lado, and Eliska Greplova
	13:00	TT 2.8	Machine determination of a phase diagram with and without deep learning — •Burak Çivitcioğlu, Rudolf A. Römer, and Andreas J. Honecker