Abstract
Digital Twins (DTs) hold transformative potential for precision cardiology by enabling patient-specific modeling of coronary hemodynamics, electrophysiological dynamics, and myocardial mechanics through multi-modal data fusion. However, their clinical deployment is impeded by critical challenges: privacy vulnerabilities in centralized data aggregation, computational inefficiency of high-fidelity simulations, algorithmic bias across demographic cohorts, and lack of robustness in distributed environments. To address these limitations, we propose QuantumFedDT-CVD, a quantum-enhanced, federated learning-enabled digital twin framework for privacy-preserving cardiovascular disease detection using consumer-grade electronics. The framework enables decentralized training across edge and clinical systems, integrating real-time physiological signals with electronic health records, genomic profiles, and medical imaging, without exchanging raw patient data. At its core, QuantumFedDT-CVD employs physics-informed quantum-variational neural operators to model non-Markovian cardiovascular dynamics with O(n log n) spectral efficiency. Evaluated on the UK Biobank cohort augmented with real-world data from 500 CVD patients, QuantumFedDT-CVD achieves an AUC-ROC of 0.94 for early adverse event prediction and a Dice score of 0.92 for cardiac segmentation. It operates at 4.2 × 108 FLOPs/round, 0.8 MB/round communication overhead, and 550 J/round energy consumption under-differential privacy. The system scales to 200 institutions, tolerates up to 40% Byzantine clients, and demonstrates a measured quantum advantage factor of 3.8, paving the way for efficient, secure, and equitable remote cardiovascular monitoring.