Geometric and Quantum Kernel Methods for Predicting Skeletal Muscle Outcomes in chronic obstructive pulmonary disease

A new kernel-geometric quantum hybrid method has been developed for predicting skeletal muscle outcomes in chronic obstructive pulmonary disease (COPD) using a cohort of 213 animals. This approach leverages synthetic symmetric positive definite references mapped through a reproducing kernel Hilbert space and low-dimensional quantum regression circuits, achieving the lowest mean root mean squared error (RMSE) for muscle weight and quality compared to classical methods like ridge/kernel models and quantum-kernel regression. This research highlights the potential of quantum machine learning in biomedical applications, particularly in enhancing prediction accuracy in small biomarker cohorts.