RESEARCH

We study models for quantum information processing and fundamental aspects of quantum information theory. One focus of our research is the theory of measurement-based quantum computation, which has resulted in a new and more thorough understanding of many-body entanglement as resource, and applications in quantum communication, quantum error correction, and quantum algorithms. Another focus lies on the application and development of machine learning in basic science and on the study of physical models for learning. Some of our work is highly interdisciplinary and addresses questions in different fields including quantum physics, robotics, behavioural biology, and the philosophy of action.

AI and Science

Quantum Computation

... and more

News & Activities

Watch online: A recent talk on Artifical Intelligence and Quantum Physics by Hans Briegel.
In this presentation, delivered at the SFB BeyondC Conference 2022, Hans Briegel talks about our research on explainable AI in science.

We have recently been awarded an ERC Advanced Grant to investigate explainability and interpretability of AI in quantum physics.

Our work on quantum machine learning beyond kernel methods has recently been published by Nature Communications.

Our paper on operationally meaningful representations for physical systems has been published in IOP Machine Learning: Science and Technology.
In this collaboration with our colleagues at the ETH, we use unsupervised learning to autonomously generate meaningful representations of physical systems such as qubits.

Our paper on "Collective operations can exponentially enhance quantum state verification" has been published in Physical Review Letters.
We have shown that having access to multiple copies of a bipartite state can exponentially reduce the number of states one needs to measure (and hence destroy) to certify their entanglement.

Preprint on Quantum Machine Learning Beyond Kernel Methods
In collaboration with colleagues from Leiden University and the MPI for Intelligent Systems, we provide a first unifying framework for all QML models, and prove that in realistic scenarios some models can be exponentially better than others.

Our collaborative paper on honeybee communication during collective defence appeared in BMC Biology
This research was done in collaboration with Morgane Nouvian and Thomas Müller at Konstanz University.