Digital health increasingly relies on artificial intelligence for clinical decision-making, patient management, and distributed learning, yet two persistent challenges remain: the computational complexity of optimization tasks, such as operating-room scheduling and genomic feature selection, and the need for robust security in federated and Internet-of-Medical-Things (IoMT) systems that are vulnerable to poisoning and spoofing attacks. Existing approaches often address these issues separately and struggle with scalability, interpretability, and resilience to advanced adversaries. This paper introduces a Hybrid Quantum-Classical Architecture (HQCA) that integrates the Quantum Approximate Optimization Algorithm (QAOA) for constrained healthcare decision-making with Quantum Digital Signatures (QDS) for secure authentication. The layered framework combines classical preprocessing with NISQ-era quantum modules and is validated under calibrated IBM Eagle-class noise models. We evaluate four representative tasks, including operating-room scheduling, genomic feature selection, federated-learning integrity, and IoMT firmware authentication, using short-depth QAOA (p≤ 3) and SWAP-test based QDS verification. HQCA reduces operating-room makespan by approximately 7.5% and halves overtime compared to classical baselines, while maintaining over 99.5% scheduling feasibility. In genomic analysis, it improves classification performance while selecting 32% less features. For security, it reduces successful poisoning in federated learning from 20.7% to 3.8%, and no spoofing events were observed in 30 trials for IoMT telemetry, with practical verification latency. These findings indicate that a unified hybrid stack can improve decision quality while providing information-theoretic authenticity guarantees with operationally feasible verification latency, demonstrating a practical pathway toward secure and efficient digital health as quantum hardware matures.
Digital health increasingly relies on artificial intelligence for clinical decision-making, patient management, and distributed learning, yet two persistent challenges remain: the computational complexity of optimization tasks, such as operating-room scheduling and genomic feature selection, and the need for robust security in federated and Internet-of-Medical-Things (IoMT) systems that are vulnerable to poisoning and spoofing attacks. Existing approaches often address these issues separately and struggle with scalability, interpretability, and resilience to advanced adversaries. This paper introduces a Hybrid Quantum-Classical Architecture (HQCA) that integrates the Quantum Approximate Optimization Algorithm (QAOA) for constrained healthcare decision-making with Quantum Digital Signatures (QDS) for secure authentication. The layered framework combines classical preprocessing with NISQ-era quantum modules and is validated under calibrated IBM Eagle-class noise models. We evaluate four representative tasks, including operating-room scheduling, genomic feature selection, federated-learning integrity, and IoMT firmware authentication, using short-depth QAOA (p≤ 3) and SWAP-test based QDS verification. HQCA reduces operating-room makespan by approximately 7.5% and halves overtime compared to classical baselines, while maintaining over 99.5% scheduling feasibility. In genomic analysis, it improves classification performance while selecting 32% less features. For security, it reduces successful poisoning in federated learning from 20.7% to 3.8%, and no spoofing events were observed in 30 trials for IoMT telemetry, with practical verification latency. These findings indicate that a unified hybrid stack can improve decision quality while providing information-theoretic authenticity guarantees with operationally feasible verification latency, demonstrating a practical pathway toward secure and efficient digital health as quantum hardware matures.
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.
The convergence of 6G communication technologies and the Internet of Medical Things (IoMT) has revolutionized modern healthcare by enabling real-time monitoring, intelligent diagnosis, and remote patient management. However, the proliferation of interconnected medical devices also exposes the ecosystem to severe security challenges, including identity forgery, data tampering, and emerging quantum-based attacks. To address these challenges, this paper proposes a blockchain-assisted quantum signature framework for securing data exchange in 6G - enabled IoMT environments. The proposed framework leverages quantum digital signatures (QDS) based on entangled photon states to ensure message authenticity and non-repudiation, while blockchain technology provides decentralized trust management and immutable transaction logging. A Medical Authorization Center (MAC) coordinates identity registration, signature verification, and consensus-based access control among IoMT entities. Furthermore, smart contracts are utilized to automate trust evaluation and audit trails, thereby mitigating insider and replay attacks. Performance analysis using Qiskit simulations and IBM’s quantum backends demonstrates an average signature fidelity of 91% and a communication efficiency of 75.61%, validating the framework’s robustness for resource-constrained medical devices. By fusing quantum communication primitives with blockchain-enabled transparency, this work presents a scalable and tamper-resistant security solution for next-generation healthcare networks. The convergence of 6G communication technologies and the Internet of Medical Things (IoMT) has revolutionized modern healthcare by enabling real-time monitoring, intelligent diagnosis, and remote patient management. However, the proliferation of interconnected medical devices also exposes the ecosystem to severe security challenges, including identity forgery, data tampering, and emerging quantum-based attacks. To address these challenges, this paper proposes a blockchain-assisted quantum signature framework for securing data exchange in 6G-enabled IoMT environments. The proposed framework leverages quantum digital signatures (QDS) based on entangled photon states to ensure message authenticity and non-repudiation, while blockchain technology provides decentralized trust management and immutable transaction logging. A Medical Authorization Center (MAC) coordinates identity registration, signature verification, and consensus-based access control among IoMT entities. Furthermore, smart contracts are utilized to automate trust evaluation and audit trails, thereby mitigating insider and replay attacks. Performance analysis using Qiskit simulations and IBM’s quantum backends demonstrates an average signature fidelity of 91% and a communication efficiency of 75.61%, validating the framework’s robustness for resource-constrained medical devices. By fusing quantum communication primitives with blockchain-enabled transparency, this work presents a scalable and tamper-resistant security solution for next-generation healthcare networks.
ABSTRACT Objective To design a secure Federated Learning (FL) framework for Internet of Medical Things (IoMT) that protects sensitive patient data from both classical and quantum attacks. Methods Proposed the QSFedMA‐IoMT protocol integrating quantum and classical security techniques. Utilized entanglement‐based E91 protocol for generating a highly secure root key to establish trust. Applied BB84 protocol for efficient generation of per‐round session keys during FL updates. Incorporated classical cryptographic scheme AES‐GCM for secure communication. Employed privacy‐enhancing techniques such as norm‐clipping and Gaussian noise to mitigate information leakage during model training. Results Our work demonstrates robust resistance against both classical and quantum adversaries, while enhancing data privacy through secure key distribution and differential privacy mechanisms. It ensures the integrity of model updates within the federated learning process and achieves an effective balance between strong security guarantees and computational efficiency, making it well‐suited for IoMT environments. Conclusion The QSFedMA‐IoMT protocol delivers a robust and practical hybrid framework for securing federated learning in healthcare systems. By integrating E91 and BB84 protocols, it strengthens key management and trust establishment. The combination of quantum security with classical privacy‐preserving techniques ensures resilience, scalability, and efficiency. Overall, this work provides a promising direction for secure and privacy‐aware federated learning in next‐generation IoMT applications.
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.
The Internet of Medical Things (IoMT) has become a core component of modern healthcare infrastructures, enabling continuous patient monitoring, remote diagnostics, and data-driven clinical decision-making. Despite these advances, authentication in IoMT environments remains a critical security challenge, intensified by strict resource constraints of medical devices and the emerging threat posed by quantum computing to classical cryptographic techniques. This systematic review investigates authentication mechanisms in IoMT from both post-quantum and system-level perspectives. A structured literature review was conducted using a PRISMA-informed methodology across major scientific databases, including IEEE Xplore, ACM Digital Library, SpringerLink, ScienceDirect, and MDPI. From an initial set of 95 records, 63 studies were selected for qualitative synthesis following screening and eligibility assessment. To organise existing research, this study introduces a multi-dimensional classification framework that categorises authentication solutions according to cryptographic paradigm (classical, hybrid, and post-quantum), deployment architecture, system objectives, and clinical operational constraints. The comparative synthesis demonstrates important trade-offs between security strength, latency, computational overhead, and energy consumption that are frequently underexplored in the existing literature. Furthermore, the analysis identifies key research gaps related to scalability in heterogeneous medical environments, trust establishment across administrative and clinical domains, usability under strict timing constraints, and resilience against quantum-capable adversaries. Based on these findings, future research directions are outlined toward adaptive, lightweight, and context-aware post-quantum authentication frameworks designed for real-world IoMT deployments. Limitations of this review include restriction to English-language publications and selected databases. This study received no external funding, and the review protocol was not formally registered.
The classic methods of encryption and ciphering techniques have a major threat due to the rapid growth of quantum computing. This is particularly true for the Internet of Medical Things (IoMT) where sensitive patients’ data needs to be protected in terms of confidentiality, integrity, and authenticity. RSA and ECC serve as two examples of public key cryptographic algorithms that are highly vulnerable to quantum attacks especially the Shor’s and Grover’s algorithm. In the context of resource constrained IoMT systems, this thesis proposes a new hash-based hybrid cryptographic algorithm that is resilient to post-quantum cryptographic (PQC) attacks. In order to achieve resistance against quantum threats the proposed method uses hash-based digital signatures such as XMSS, SPHINCS+, or symmetric encryption like AES-256 with higher key sizes. In the Qiskit environment (Python, Anaconda Navigator, and Jupyter Notebook), Grover’s algorithm is modeled to simulate quantum threats. The proposed method is tested through quantum circuit analysis to evaluate their robustness. Our results will prove that the hybrid scheme, while remaining lightweight and suitable for real-time medical applications, significantly lowers the efficiency gains of quantum attacks. The proposed method will bridge the gap between the theoretical world of PQC and the practical world of IoMT networks, improving the long-term security and trust in critical healthcare systems.