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.
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.
Hybrid quantum-classical intelligence is a new promising paradigm to develop personalized smart healthcare systems with exemplification of computational power of quantum computing and scalability of classical artificial intelligent. This chapter introduces a hybrid design based on quantum machine learning, healthcare analytics, explainable AI, federated learning, and intelligent clinical decision support to apply precision medicine via this design. The given architecture will allow analyzing multimodal healthcare data efficiently, predicting the disease-risk, optimizing treatment plans on a personal scale, interpreting medical images, and predictive healthcare analytics. The framework enables the next-generation smart healthcare ecosystems by improving the computational efficiency, decision accuracy, transparency, and preserving privacy to enhance the realization of intelligent, patient-centric healthcare services.
The rapid pace of development in healthcare artificial intelligence requires architectures that transcend predictive analytics to better understand causality and generate simulations of patient trajectories. This study proposes the Quantum Edge Federated Graph Transformer (QFGT), a hybrid quantum-inspired approach aimed at empowering causal, generative, and privacy-preserving digital twin healthcare. The model incorporates quantum kernel attention for complex-amplitude feature embedding, graph transformer reasoning mechanism for relational inference over multimodal clinical entities, and federated optimization for secure multi-institutional learning. Quantum-inspired kernel mappings in Hilbert space encode entangled dependencies between laboratory, imaging, genomic, and clinical text data, while a causal regularization layer constrains learned representations to adhere to interpretable cause-and-effect relations learned from structural causal models. The generative digital-twin module uses a diffusion-based latent simulator that predicts personalized trajectories of the disease, and it supports what-if counterfactual interventions. Federated deployment at the edge healthcare nodes enables model training with data decentralization and strict compliance with HIPAA and GDPR privacy regulations. Experimental work on multimodal clinical data shows an accuracy of 98.1% with a mean early-detection window of 7.9 months and F1-scores >0.98 for all diseases, in addition to an increase in minority-cohort recall of 6.5% via equitable quantum-kernel feature sharing. The proposed QFGT framework opens up a new direction for the quantum-inspired, federated, and causally explainable digital twin systems, which lead to trustworthy, proactive, and personal healthcare intelligence at the quantum edge.
Quantum-Enhanced Artificial Intelligence (QAI) integrates quantum computing with advanced AI to tackle the increasing complexity of healthcare data. The chapter discusses the impact of QAI in medical diagnostics, precision medicine, drug discovery and clinical decision support systems. It also explores the use cases in the Internet of Medical Things (IoMT), digital twins, and predictive healthcare analytics. In addition, current challenges, ethical issues and future directions are identified, illustrating the possibilities of QAI to empower next-generation intelligent medical systems to provide faster, more accurate and personalized healthcare.
The convergence of quantum computing and artificial intelligence is creating new opportunities for smart healthcare, particularly in medical image analysis, privacy-preserving data management, and clinical decision support. This chapter presents a conceptual and architectural overview of quantum machine learning (QML) for a multidisciplinary audience. It introduces quantum computing fundamentals and discusses quantum kernel methods and variational quantum circuits using the MedMNIST benchmark. A geometric separation diagnostic is proposed as a conceptual, computationally inexpensive, hardware-independent screening construct for indicating whether quantum encoding places data in a label-separable configuration, with its empirical validation identified as future work. The chapter further examines privacy-preserving healthcare through federated learning, synthetic data governance, differential privacy, post-quantum cryptography, and regulatory considerations, and presents a five-layer quantum-enhanced healthcare architecture together with key challenges and future research directions.
This chapter presents an integrated quantum–federated intelligence framework for secure smart healthcare and smart city infrastructures. The proposed architecture combines edge IoT sensing, quantum-enhanced anomaly detection, federated learning orchestration, and quantum-secure communication layers. Hybrid quantum–classical models improve zero-day detection and high-dimensional pattern separability, while privacy-preserving federated mechanisms ensure data locality and regulatory compliance. Performance metrics, deployment constraints, and NISQ-era challenges are analyzed, outlining a practical roadmap toward scalable and resilient cyber-physical systems.
The growing volume of biomedical data from electronic health records, medical imaging, genomic sequencing, wearable Internet of Medical Things (IoMT) devices, and precision medicine has created new opportunities as well as significant computational challenges for healthcare. This chapter explores how Quantum Machine Learning (QML) can complement conventional machine learning by improving the analysis of complex, high-dimensional healthcare data through hybrid quantum–classical approaches. It introduces the fundamentals of quantum computing and discusses their application in genomics, medical imaging, drug discovery, IoMT-enabled healthcare, and cybersecurity. The chapter also presents an integrated framework that brings together quantum-enabled analytics, secure communication, and clinical decision support, while highlighting current challenges, ethical considerations, and future directions such as fault-tolerant quantum computing, explainable quantum AI, federated quantum learning, and post-quantum healthcare security.