A REAL TIME HYBRID QUANTUM CLASSICAL DIAGNOSTIC FRAMEWORK FOR EARLY DISEASE DETECTION

Early detection of clinical deterioration is crucial for better patient outcomes, and current diagnostic models struggle to model the complex interactions observed in nonlinear patterns in multimodal medical data while meeting the performance requirements for real-time operation. This paper proposes a Real-Time Hybrid Quantum-Classical Diagnostic Framework (HQCDF) to improve early disease detection by combining a Quantum Variational Biomarker Embedding module (QVBE), a Classical Temporal-Clinical Learning module (CTCLM), and a novel Quantum-Classical Diagnostic Fusion mechanism (QCDF). A semi-synthetic multimodal dataset, Med-EarlyQ, physiological time series, lab biomarkers, and clinical scores was built to evaluate. The hybrid model has been trained using End-to-end differentiable quantum-classical optimization via the parameter-shift rule. Results showed that HQCDF obtained 96% accuracy, 0.93 F1-score, and 0.95 AUROC to outperform state-of-the-art classical deep learning models and existing hybrid approaches by 6~15% while controlling the real-time inference latency to 18 ms. Analysis revealed that the QCDF module contributed to increased robustness against quantum noise and missing clinical information, and to QE's increased sensitivity to subtle trends in disease in its early stages. The results show that the proposed hybrid framework is a promising evolution, addressing the call for next-generation, real-time, and resource-efficient diagnostic systems with great potential for early intervention and clinical decision-support impact.