The QuditML research project is dedicated to the study of so-called qubits, i.e. multi-level quantum systems. While the majority of quantum algorithms to date have focused on binary qubits, many quantum hardware platforms inherently support more than two states. QuditML aims to systematically harness this untapped potential for machine learning applications. The project is funded by the Austrian Science Fund FWF.
Compared to traditional qubit approaches, qubits promise a higher information density, a greater variety of possible computing operations and potentially more efficient and robust algorithms. At the same time, their use poses new challenges in terms of algorithm design, data coding and training. Especially in the field of quantum machine learning, practical applications and systematic investigations with real data are still lacking. RISC Software GmbH is addressing precisely this research gap with QuditML.
The QuditML project team from left to right: Dr. Michael Giretzlehner (Head of Research Unit Medical Informatics), Dominik Freinberger MSc (Researcher & Developer), DI Philipp Moser PhD (Senior Researcher)
In the QuditML project, Qudit-based machine learning algorithms for classification and regression tasks are developed, implemented and systematically analyzed. These methods are evaluated using real data sets from three scientific application areas:
QuditML is designed as a high-risk/high-gain project and aims to achieve significant methodological advances in the field of quantum machine learning. By transferring the theoretical advantages of Qudits into practical machine learning algorithms for the first time, the project is intended to act as a catalyst for more intensive research into multi-level quantum computing. In the long term, QuditML will strengthen the scientific excellence and international visibility of RISC Software GmbH.
Workflow in the QuditML project
This project is funded by the Austrian Science Fund FWF.
Researcher & Developer
