Protecting early IP in quantum machine learning for healthcare

DryLabz has submitted a patent application related to quantum machine learning in clinical modeling.

The work emerged during our participation in the QAI Ventures accelerator, where we explored how quantum machine learning could contribute to healthcare use cases, particularly in settings where data is limited, cohorts are small, and clinical decisions carry high stakes.

As part of this exploration, we developed a pipeline that combines classical survival models with quantum-generated features. The approach was built as an end-to-end workflow and evaluated through benchmarking, including execution on real quantum hardware.

For DryLabz, this patent application is not only about protecting early intellectual property. It also creates space for longer-term exploration in an emerging field that may become increasingly relevant for clinical prediction, patient stratification, and decision support.

The work was developed in close collaboration with experts from the quantum ecosystem, including QuantumBasel, QAI Ventures, and additional technical and scientific contributors. We are grateful for the guidance, sparring, and hands-on support that helped shape both the technical work and the broader IP strategy.

While our near-term focus remains on clinically grounded decision support with hospitals, laboratories, and physicians, this patent application reflects our ambition to build DryLabz with a strong innovation foundation for the future.