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 the physics department at the University of Regensburg and of PGI-8 at FZ Jülich.
News
CQS @ DPG Fühjahrstagung 2026
Learn about our group’s work at the DPG Spring Meeting 2026:
- Optimization and Representability of time-dependent Neural Quantum States: a study of the 1D critical quantum Ising model
- Compression of Floquet random circuits
- Enhancing efficiency of local-information time evolution
- Operator Lanczos approach enabling neural quantum states as real frequency impurity solvers
- Study of a two-dimensional Rydberg array in a cavity with neural quantum states
- Reinforcement learning entangling operations on spin qubits
Sven joined as new Postdoc
We welcome Sven, who joins our team as a new Postdoc. Sven will investigate the inner workings of neural quantum states and what this means for time evolution.
DFG Research Unit FOR5919 approved
The DFG has decided to fund our Research Unit FOR5919 “Machine Learning for Complex Quantum States”. This collaborative research initiative aims at developing machine-learning-enhanced techniques for simulation, data analysis, and control to enable new insights into complex quantum states and dynamics. It involves participants from eleven institutions across Germany and Switzerland. We’re very excited to kick it off!
Massimo joined as new PhD student
Our group continues growing: Massimo joined us as a new PhD student. He will work on investigating the dynamics of correlated two-dimensional materials with neural quantum states.
Explicitly time-dependent NQS
Our article on explicitly time-dependent NQS has been published in Machine Learning: Science and Technology. Instead of conventional forward-integration of Schrödinger’s equation, this technique optimizes an artificial neural network to solve the dynamics simultaneoulsy across a whole time interval. We demonstrated the efficiency of the approach by simulating quench dynamics and a time-dependent control protocol for a large two-dimensional quantum magnet.
Paper on roughening dynamcis published
Thanks to great efforts by Wladi, our work on roughening dynamics now appeared in Physical Review Letters! In this paper, we describe how the smooth-interface regime below the roughening transition of the two-dimensional quantum Ising model leads to prethermal plateaux in the non-equilibrium dynamics of domain wall initial conditions.
Stefan and Valentin joined us for Master projects
Two new Master students joined our group: Stefan will work on aspects of many-body quantum chaos and Valentin will continue to develop our explicitly time-dependent NQS approach.
Postdoc opening
We are looking for a motivated postdoc to join our team and work on new approaches to tackle the quantum many-body problem with machine learning techniques. See the opening for details.
Update: Review of applications has started.
Noé joined as new PhD student
With the beginning of October, Noé joined our group as a new PhD student. He is going to work on our DFG project ``Machine Learning to Tailor Correlated States of Matter’'.
Workshop on NQS for dynamics coming up
In November we’ll be hosting a workshop on simulating the non-equilibrium dynamics with neural quantum states here in Regensburg. See the workshop website for more information!