Welcome to the pages of our Computational Quantum Science research group! Our research covers various aspects of complex quantum systems, especially non-equilibrium settings. We have a strong focus on the development of computational methods that leverage the latest developments in machine learning for this purpose. Our group is part of PGI-8 at FZ Jülich.
Our group is expanding and we are currently looking to fill several Postdoc/PhD positions. One job ad is already posted on the FZJ job portal. To learn about further opportunities, just contact Markus.
Update: The Postdoc position has been filled. If you are looking for a PhD opportunity, check out our opening in the area of digital quantum simulation!
Qiskit Seminar on exploring the Jarzynski relation on a quantum computer
Last week, Dominik Hahn (MPI PKS) presented the outcomes of our collaboration on the quantum many-body Jarzynski equality in the Qiskit Seminar Series. The recording is available on youtube.
Quantum ideas factory
During the next two days Markus will mentor a team of talented students and young researchers, who will tackle the quantum control problem with reinforcement learning approaches at the scientific hackathon of the Quantum Ideas Factory 2022 in Heidelberg. Thanks to Mo for the help to set up the challenge. That’s going to be fun!
Paper on quantum phase transition dynamics published
Wladislaw Krinitsin joined
Today, Wladislaw Krinitsin joined our group as a new PhD student. He will work on neural quantum states for non-equilibrium dynamics. Welcome Wladi!
Helmholtz Young Investigator group started
Today, we are launching our Helmholtz Young Investigator Group “Machine Learning for Quantum Technology” at PGI-8 (FZ Jülich). Within this project we are going to develop machine learning techniques for the advancement of quantum technologies with a focus on two promising applications: On the one hand, we will employ neural quantum states for simulations of non-equilibrium quantum matter, and, on the other hand, we will develop a versatile framework for optimal quantum control based on reinforcement learning.