Quantum Algorithms Applied to Healthcare
We develop hybrid quantum-classical algorithms to address real-world challenges — from uncovering complex patterns in biomedical data to optimizing treatment pathways in uncertain conditions.
Quantum feature embedding for structured biomedical signals
Specialized tool that transforms structured biomedical signals—such as EEG, ECG, gene expression, or multi-sensor physiological data—into quantum-enhanced feature spaces. This allows advanced machine learning models to detect subtle, nonlinear patterns that are often hidden from conventional algorithms.
Variational hybrid circuits adapted for today’s NISQ hardware
The circuits are designed to solve optimization and pattern recognition problems in biomedical and clinical data, using short-depth quantum circuits guided by classical feedback loops.
This hybrid structure allows organizations to benefit from quantum processing today, without waiting for future error-corrected quantum hardware.
Robust optimization frameworks combining quantum sampling with classical training
These frameworks combine the probabilistic exploration of quantum circuits (quantum sampling) with the stability and efficiency of classical training loops to navigate challenging optimization landscapes.