Due to a new method, artificial neural networks, as used in machine learning, will be able to be trained quicker so as to be able to solve complex problems in quantum mechanics. For example, previously unexplained properties of a special state of matter, the quantum spin liquid, can be calculated, something that has not been possible with any previous method to date. This has been made possible by a new optimisation method developed by the Institute of Physics.

Prof. Dieter Vollhardt was awarded an honorary doctorate by the University of Warsaw last week in recognition of his scientific achievements and longstanding collaboration with theoretical physicists at the University of Warsaw.

The Centre for Electronic Correlations and Magnetism (EKM) was established in the early 1990s. Since then, it has become a top research institute in the field of quantum mechanics. The meeting of the scientific advisory board of the EKM in Augsburg, composed of leading experts in the field, confirms this.

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.

A fundamental challenge in digital quantum simulation (DQS) is the control of an inherent error, which appears when discretizing the time evolution of a quantum many-body system as a sequence of quantum gates, called Trotterization. Here, we show that quantum localization-by constraining the time evolution through quantum interference-strongly bounds these errors for local observables, leading to an error independent of system size and simulation time.

We show how lattice gauge theories can display many-body localization dynamics in the absence of disorder. Our starting point is the observation that, for some generic translationally invariant states, the Gauss law effectively induces a dynamics which can be described as a disorder average over gauge superselection sectors.