Description

Research focus: strongly interacting quantum many-body systems, which I study using a combination of numerical methods, machine learning techniques and quantum simulation.

We aim to understand strongly-interacting quantum many-body systems with novel techniques. To this end, we combine state-of-the art numerical methods established in condensed matter research, intuitive physical pictures, close collaboration with quantum simulation experiments, and machine learning techniques, such as for example neural networks.

Quantum simulation experiments offer a new perspective on strongly-correlated many-body systems: through a high degree of control and tunability, microscopic models can be directly realized. In many experiments, readout with single-site resolution is possible, enabling a direct real-space view on condensed matter problems.

For the most difficult questions, it can even be challenging to find the right questions to ask, and the right observables to measure. For example from Fock space snapshots of a quantum many-body system, correlations up to arbitrary order can now be studied. Quantum simulation experiments also provide a bridge between theoretical models and real materials. Similar probes as in condensed matter experiments, such as spectroscopy, can be implemented in quantum simulators in systems which realizes a clean microscopic model.

We aim to identify measurements and probes which enable new insights and simultaneously develop and apply numerical tools to simulate microscopic systems of interest.