Today at the American Heart Association (AHA) Scientific Sessions 2025 in New Orleans, I had the privilege to present the first-ever study applying quantum computing–based machine learning to congenital heart disease (CHD) or echocardiographic imaging — and one of the very first in cardiology as a whole.

We developed a hybrid deep learning model that integrates quantum computing (QC) into a convolutional neural network for echocardiographic image analysis. Using real-world data from patients with complex congenital and structural heart disease, the QC-model outperformed conventional deep learning approaches — achieving superior accuracy and diagnostic differentiation across challenging categories.

This work is a step toward bringing quantum technology into precision medicine for cardiology (AHA 2025: MP737).

As part of our broader strategy, this study directly connects to our recently established high-performance computing infrastructure in Münster which allows us to combine on-premise AI with quantum cloud computing for truly cutting-edge analysis.

The project was made possible through generous support from the EMAH Stiftung Karla Völlm, whose commitment continues to drive innovation in congenital heart disease research.

This is only the beginning —we are thrilled to further develop this pioneering approach together with Prof. Mihai Udrescu and the team at the Politehnica University of Timișoara. We’re also very proud to announce the launch of a new Quantum Computing Fellowship, funded by the Karla Völlm Foundation, to advance this pioneering work at the interface of AI, quantum technology, and congenital cardiology. A heartfelt thank you to all collaborators and supporters who made this milestone possible.

 

Watch the space - Quantum computing today is likely where AI was 10 years ago.