News

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!

Jonas distinguished as Young Excellent Scientist

Our Postdoc Jonas has secured one of the five places in FZ Jülich’s Young Excellent Scientist Program (YESP). The program offers individual support modules, among which the funding to hold their own symposium on a topic of their choice. Congratulations, Jonas!

Opening for PhD position

We are looking for a new PhD student for a DFG-funded project on “machine learning to tailor correlated states of matter”. The project in cooperation with colleagues at MPI PKS will evolve around neural quantum states for non-equilibrium quantum many-body physics.

Update: The position has been filled.

Introducing wave function networks

The advent of quantum simulators and computers has made projective measurement a reality at the many-particle level: Experiments routinely collect high-quality pictures of wave functions made of hundreds of components. But the wealth of information obtained challenges traditional theoretical modeling, which often focuses on a few observables. In our work, that is now published in Phys. Rev. X, we introduce a framework to describe wave-function measurements using network theory, enabling the discovery of a very deep inner structure in quantum wave functions: scale-freeness similar to other completely disconnected ensembles, such as those found in communication, social networks and the internet.

Opening for student assistant

We are lookging for a dedicated student assistant to support our research team. The main tasks will evolve around programming and conducting explorative studies in the area of machine learning for quantum science.

Update: The position has been filled.

Learning Hamiltonians that describe non-equilibrium steady states

Periodic driving is a prime way to engineer quantum matter with interesting properties. For this purpose, it is essential to bring the system into metastable steady states, the properties of which governed by a corresponding effective Hamiltonian. We have developed a deep-learning-assisted variational algorithm to reconstruct such effective Hamiltonians from observations. Thereby, we could recover local Hamiltonians in pre-thermal regimes and observe the growing complexity of instantaneous effective Hamiltonians in subsequent heating regimes. Our findings are now published in Phys. Rev. Research.