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 and the FIDS at the University of Regensburg.
Observing fluctuation relations on a quantum processor
Jarzynski’s equality describes a relation in statistical physics between the fluctuations during a work process and the thermal free energy difference corresponding to the initial and final ensemble. While Jarzynski’s equality has been checked in many scenarios, previous experiments focused on systems with only a handful of degrees of freedom in the quantum regime. In our collaboration with colleagues at MPI PKS, the University of Bonn, and UC Berkeley/LBNL, we probed for the first time Jarzynski’s equality in the quantum many-body regime. We devise a dynamical protocol to measure work on quantum computers using a suitably prepared thermal ensemble and tested Jarzynski’s equality on different quantum processors with up to 16 qubits. The article has now been published in Phys. Rev. X.
Combined school and conference at ICTP
Together with colleagues from Hamburg, Waterloo, and Augsburg, we are organizing a school and conference “Frontiers at the intersection of quantum simulation and machine learning” to be held from Apr 8 to 19 2024 at the International Centre for Theoretical Physics (ICTP) in Trieste. This will be the first event at ICTP co-sponsored by the WE Heraeus Foundation. More detailed information will follow soon on the event website.
Jonas joins us as a Postdoc
Our group keeps growing and today is Jonas Rigo’s first day as a Postdoc. Welcome, Jonas!
Simulating spectral functions with NQS
We developed a new approach to simulate spectral functions using neural quantum states, which for example accurately captures the gap closing at the phase transition of a two-dimensional quantum magnet despite the associated diverging time scale. The paper appeared today in Phys. Rev. Lett.!
Mo graduated and continues to pursue a PhD
Last week, Mo Abedi successfully defended his Master’s thesis and he will stay with us to pursue his PhD. In his research, he will continue to develop Reinforcement Learning approaches for optimal quantum control. Congratulations, Mo!
Today, Francesca de Franco joined our group as a new PhD student. She will explore how one can use near-term quantum devices to study many-body physics. Welcome Francesca!
Tutorial and Focus Session at DPG Spring Meeting
For ML+Quantum Physics enthusiasts and all those who would like to learn about it, we are organizing a Tutorial Session “Physics Meets Machine Learning” and a Focus Session “Machine Learning for Complex Quantum Systems” at the DPG Spring Meeting in Dresden. There will also be another Focus Session on “Understanding Machine Learning as Complex Interacting Systems”. All these sessions promise to be very interesting with great invited speakers – so come by!
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: All positions were filled in the meantime.
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.