Geometric and Quantum Kernel Methods for Predicting Skeletal Muscle Outcomes in chronic obstructive pulmonary disease
arXiv:2601.00921v3 Announce Type: replace-cross Abstract: Chronic obstructive pulmonary disease (COPD) affects hundreds of millions of people worldwide, and skeletal-muscle dysfunction is clinically important. Quantum machine learning is increasingly explored for biomedical prediction, but its value in small biomarker cohorts requires benchmarking against strong classical baselines. We analysed a cigarette-smoke COPD cohort of 213 animals with blood and bronchoalveolar-lavage biomarkers to predict tibialis anterior muscle weight, muscle quality, and force. We developed a kernel-geometric quant
The paper demonstrates the growing maturity of quantum machine learning applications, specifically in biomedical prediction, and its comparative benchmarking against classical methods.
This research highlights the potential of quantum machine learning to address complex health challenges and accelerates the integration of advanced computational methods into medical research.
The explicit comparison of quantum kernel methods with classical baselines in a clinically relevant context offers a clearer path for validating and adopting quantum machine learning in healthcare.
- · Quantum computing researchers
- · Biomedical research
- · Machine learning in healthcare
- · Pharmaceutical industry
Increased investment and research into quantum machine learning for drug discovery and personalized medicine.
Development of specialized quantum hardware optimized for biomedical and healthcare applications.
Ethical and regulatory debates around explainability and bias in quantum-powered diagnostic tools.
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