QUANTUM KERNEL METHODS IN BIOMEDICAL CLASSIFICATION: NEW PERSPECTIVES ON HARDWARE-AWARE COMPUTATION AND AUTOIMMUNE DISEASE DIAGNOSIS

Quantum kernel methods (QKMs) are emerging as a promising approach for addressing complex classification challenges in biomedical research. By mapping classical biomedical data into high-dimensional quantum Hilbert spaces, QKMs can reveal nonlinear patterns that are often obscured in traditional machine learning models. This study synthesizes recent theoretical and empirical advances in quantum kernel design, hybrid quantum–classical learning, and hardware-aware optimization with a focus on autoimmune disease diagnosis. Through a structured review and thematic synthesis, four key findings are highlighted: quantum feature maps demonstrate superior expressivity for small, high-dimensional biomedical datasets; hardware-efficient circuit design is essential to counteract noise and qubit limitations in NISQ devices; preprocessing and governance practices, including biologically informed feature selection and privacy-preserving data integration, improve model robustness; and (4) explainability frameworks adapted from classical XAI enhance trust and interpretability in quantum-assisted biomedical inference. The findings converge on a proposed hardware-aware QKM pipeline that integrates domain-specific preprocessing, efficient quantum circuit construction, and interpretable post hoc validation. This approach bridges the gap between theoretical quantum advantage and clinical applicability, offering a practical framework for next-generation biomedical classification systems capable of handling autoimmune and other complex diseases with transparency and computational efficiency.