Bereiche | Tage | Auswahl | Suche | Aktualisierungen | Downloads | Hilfe
TT: Fachverband Tiefe Temperaturen
TT 17: Correlated Electrons: Method Development
TT 17.7: Vortrag
Dienstag, 18. März 2025, 11:00–11:15, H33
Investigating Quantum Many-Body Systems with Neural Quantum States — •Fabian Döschl1,2, Felix A. Palm1,2,3, Hannah Lange1,2,4, Fabian Grusdt1,2, and Annabelle Bohrdt2,5 — 1Ludwig-Maximilians-University Munich, Theresienstr. 37, Munich D-80333, Germany — 2Munich Center for Quantum Science and Technology, Schellingstr. 4, Munich D-80799, Germany — 3CENOLI, Universite Libre de Bruxelles, CP 231, Campus Plaine, B-1050 Brussels, Belgium — 4Max-Planck-Institute for Quantum Optics, Hans-Kopfermann-Str.1, Garching D-85748, Germany — 5University of Regensburg, Universitätsstr. 31, Regensburg D-93053, Germany
Neural Quantum States (NQS) have shown to be a reliable and efficient method for numerically simulating the ground states of two-dimensional quantum systems. Of particular interest for current research are fractional quantum Hall models and lattice gauge theories, both of which present significant challenges for state-of-the-art numerics. In this study, we demonstrate that NQS are capable of effectively simulating such complex systems. We focus on evaluating the strengths and weaknesses of this Ansatz from a physical perspective, providing deeper insights into the potential difficulties encountered during optimization.
Keywords: Neural Quantum States; Machine Learning; Fractional Quantum Hall