Dem theoretischen Physiker Dieter Vollhardt wurde in den USA die "2022 Feenberg Memorial Medal" verliehen. Vollhardt, ehemaliger Inhaber des Lehrstuhls für Theoretische Physik III/Elektronische Korrelationen und Magnetismus am Institut für Physik der Universität Augsburg, erhielt die hohe Auszeichnung zusammen mit Antoine Georges (Frankreich) und Gabriel Kotliar (USA).

Ein internationales Team von Physikern unter Beteiligung der Universität hat erstmals eine wichtige theoretische Vorhersage der Quantenphysik bestätigt. Die Berechnungen dazu sind so komplex, dass sie bislang selbst Supercomputer überforderten. Den Forschern gelang es jedoch, sie mit Methoden aus dem Bereich der künstlichen Intelligenz deutlich zu vereinfachen.

Digital quantum simulation on quantum computers provides the potential to simulate the unitary evolution of any many-body Hamiltonian with bounded spectrum by discretizing the time evolution operator through a sequence of elementary quantum gates. A fundamental challenge in this context originates from experimental imperfections, which critically limits the number of attainable gates...

In this work, we combine quantum renormalization group approaches with deep artificial neural networks for the description of the real-time evolution in strongly disordered quantum matter. We find that this allows us to accurately compute the long-time coherent dynamics of large many-body localized systems in nonperturbative regimes including the effects of many-body resonances.

Disorder-free localization has been recently introduced as a mechanism for ergodicity breaking in low-dimensional homogeneous lattice gauge theories caused by local constraints imposed by gauge invariance. We show that also genuinely interacting systems in two spatial dimensions can become nonergodic as a consequence of this mechanism.

The efficient numerical simulation of nonequilibrium real-time evolution in isolated quantum matter constitutes a key challenge for current computational methods. This holds in particular in the regime of two spatial dimensions, whose experimental exploration is currently pursued with strong efforts in quantum simulators. In this work we present a versatile and efficient machine learning inspired approach based on a recently introduced artificial neural network encoding of quantum many-body wave functions.