Quantum-Enhanced Federated Digital Twins for Smart Healthcare Privacy-Preserving Predictive Medicine
Rajinder Kumar, Manita Paneri, Manpreet Kaur
This chapter develops an integrated framework for quantum-enhanced federated digital twins in Internet of Medical Things (IoMT)-based smart healthcare systems. It explains how continuous sensing, edge intelligence, federated learning, digital twin modeling, and near-term hybrid quantum-classical machine learning can be combined to support privacy-preserving predictive and personalized medicine. The chapter argues that the value of quantum-enhanced healthcare should not be framed as immediate quantum superiority, but as a disciplined pathway for testing quantum kernels, variational circuits, quantum-inspired optimization, and hybrid classifiers against strong classical baselines. It presents a layered architecture, clinical use cases, security safeguards, implementation challenges, and research directions. The chapter provides a clinically grounded and ethically cautious roadmap for researchers, healthcare AI developers, hospital leaders, and policymakers seeking trustworthy intelligent healthcare without centralizing sensitive patient data.
Advances in computational intelligence and robotics book series•Oct 23, 2026•0 citations