Sovelletun fysiikan laitoksen tutkimusseminaari: Eliška Greplová
Prof. Eliška Greplová is an Assistant Professor at the Department of Quantum Nanoscience at Delft University of Technology. In her newly established "Quantum Matter and AI" group she works at the intersection of quantum technologies, artificial intelligence and condensed matter physics.
Precise verification and parameter estimation of quantum devices are critical for further technological progress in simulation and understanding of quantum matter. In this presentation, I will discuss how learning algorithms can be used efficiently for this task. We show how to use Bayesian learning and neural networks to reconstruct the physics of large scale out-of-equilibrium quantum systems in the context of quantum simulation with ultracold atoms. We discuss how this class of learning methods can be generalised for other quantum device calibration purposes, such as the characterization of energy levels of quantum dots in bilayer graphene. Finally, we show how machine learning algorithms can also directly control quantum experiments, which we demonstrate for automated tuning of quantum dots.
Host: Prof. Jose Lado
Zoom link available on request to [email protected].
Everybody interested welcome to attend!