This group, led by Martin Gärttner, studies theoretical foundations of quantum technologies. We focus on quantum simulation, machine learning methods in quantum technologies, and development of numerical methods for quantum many-body dynamics, and collaborate closely with experimental groups.
We theoretically study non-equilibrium dynamics of complex quantum systems, focussing on ultra cold gases near 0K temperature, highly excited Rydberg atoms, opto-mechanical devices and hybrid assemblies of these.
Our research lies at the intersection of many-body dynamics, quantum simulation, quantum control, and applications of machine learning in physics. We are interested in problems of both fundamental nature and immediate applications. We develop approximate analytical methods, and design numerical techniques in order to investigate different problems in quantum dynamics. We collaborate with theory groups and experimental labs to test our theoretical predictions against experiment.
KU is seeking an experienced & highly motivated Research Scientist or Postdoc Associate to work on a funded project to investigate and develop quantum-inspired type algorithms, for Partial Differential Equations (PDEs) in view of applications to nuclear reactor simulations. The specific class of quantum-inspired methods on which we will focus is tensor networks.