Compensating Connectivity Restrictions in Quantum Annealers via Splitting and Linearization Techniques
By Marcel Seelbach Benkner, Zorah Lähner, Vladislav Golyanik, Martin Kliesch, Michael Moeller
Published in arxiv:2507.12536.
Geometry in Machine Learning
Research Group at the University of Bonn and Lamarr Institute
The Geometry in Machine Learning group works at the intersection of machine learning, computer vision and computer graphics. Our research focuses on developing novel techniques that leverage geometric insights to enhance the capabilities of machine learning models. By studying the underlying geometric structures of data, we aim to create more robust, efficient, and interpretable algorithms. Our work spans a wide range of topics, including geometric representation learning, classic geometry processing, and learning under symmetry, as well as applications in physics.
