Hybrid Quantum-Classical Architectures in Medical Imaging: A Taxonomy-Based Survey of COVID-19 Models

This paper surveys the emerging field of hybrid quantum–classical (HQC) architectures for medical image analysis, with a particular focus on COVID-19 classification as a rep-resentative application. Despite extensive progress in quantum machine learning, there is still a lack of structured taxonomy-driven analysis that systematically characterizes how quantum components are integrated within hybrid imaging pipelines. This study pro-vides a structured review of representative HQC models, categorizing existing approaches based on the functional role of quantum modules, including front-end processing, feature extraction, and classification layers. A comparative analysis is conducted to examine how these architectural design choices relate to reported performance across different ex-perimental settings. The review shows that hybrid models can achieve strong binary classification performance under simulation conditions, particularly when quantum cir-cuits are used as feature extractors. However, performance in multi-class scenarios remains inconsistent and highly sensitive to encoding strategies, circuit design, and optimization settings. In addition, most existing studies rely on simulator-based evaluations, limited or single-source datasets, and non-standardized benchmarking protocols, which restrict reproducibility and generalization. Real-hardware validation remains scarce, and no consistent evidence of task-level quantum advantage over optimized classical baselines has been demonstrated. Overall, current HQC-based approaches should be considered exploratory rather than clinically validated solutions. Future progress requires stand-ardized benchmarking, multi-institutional validation, hardware-aware evaluation, and the development of interpretable hybrid architectures that respect NISQ-era constraints.