QML4Med: Quantum computing meets machine learning in medicine

The research project QML4Med aims to explore the potential of the promising fusion of Quantum Computing and Machine Learning in the medical context. While Machine Learning is already established as a valuable auxiliary tool in medicine, Quantum Computing is still in an earlier developmental phase. This project is funded by the FFG Austrian Research Promotion Agency.

Challenges in Integrating Quantum Computing into Medicine

RISC Software GmbH will investigate three clinical use cases on simulated and real quantum hardware:

1. 1. Prediction of complications following blood transfusions
2. 2. ECG diagnostics, e.g., detection of arrhythmias
3. 3. Pathology detection from image data

The project will analyze achieved model accuracies, the impact of noise interference, quantum encoding methods, and model explainability (explainable AI). QML4Med aims to identify promising categories of medical problems for Quantum Machine Learning with quantum algorithms and hardware that will be available in the medium term.

Long-term Vision: Steering Future Research and Development Activities

In the long term, QML4Med will significantly influence subsequent R&D&I activities, strengthen (regional) collaborations with industry and research, and make a significant contribution to Austria’s technological sovereignty in the field of quantum computing.

Schematic workflow in the QML4Med project

Project Partners

Contact Person

Project Lead

DI Philipp Moser, PhD

Researcher & Developer