Secure, privacy-preserving sharing and analysis of health data is foundational to precision medicine, population health, and telehealth services. Emerging quantum networks promise fundamentally new communication primitives (entanglement, quantum key distribution) that when integrated with advanced AI architectures can enable end-to-end secure healthcare systems with improved confidentiality, tamper resistance, and novel distributed computation modes. This article develops a systematic framework for integrating quantum communications and cryptography with classical and hybrid quantum–classical AI models in healthcare. We (a) summarize the enabling quantum network technologies and AI building blocks, (b) propose layered system architectures and protocols for secure data exchange and collaborative learning, (c) formalize threat models and privacy requirements in healthcare contexts, (d) discuss algorithmic integrations (federated learning with QKD, quantum-enhanced machine learning, secure multiparty quantum protocols), (e) provide evaluation metrics and prototype deployment roadmaps, and (f) identify research gaps and regulatory implications. The treatment is scholarly yet practical, aimed at researchers, engineers, and healthcare stakeholders planning future-proof secure AI systems. (Keywords: quantum networks; quantum key distribution; quantum machine learning; federated learning; healthcare security; privacy.)
Secure, privacy-preserving sharing and analysis of health data is foundational to precision medicine, population health, and telehealth services. Emerging quantum networks promise fundamentally new communication primitives (entanglement, quantum key distribution) that when integrated with advanced AI architectures can enable end-to-end secure healthcare systems with improved confidentiality, tamper resistance, and novel distributed computation modes. This article develops a systematic framework for integrating quantum communications and cryptography with classical and hybrid quantum–classical AI models in healthcare. We (a) summarize the enabling quantum network technologies and AI building blocks, (b) propose layered system architectures and protocols for secure data exchange and collaborative learning, (c) formalize threat models and privacy requirements in healthcare contexts, (d) discuss algorithmic integrations (federated learning with QKD, quantum-enhanced machine learning, secure multiparty quantum protocols), (e) provide evaluation metrics and prototype deployment roadmaps, and (f) identify research gaps and regulatory implications. The treatment is scholarly yet practical, aimed at researchers, engineers, and healthcare stakeholders planning future-proof secure AI systems. (Keywords: quantum networks; quantum key distribution; quantum machine learning; federated learning; healthcare security; privacy.)
The emergence of 5G networks has revolutionized healthcare delivery by enabling communication with ultra-low latency, massive connectivity, and quick access to IoT devices' medical data. The digitalization of healthcare has, however, introduced privacy, data security, and scalability as major challenges, and the issues have been worsened by the rapid digitalization of the healthcare industry. The current review paper is a comprehensive evaluation of the role of Quantum Machine Learning (QML) as a paradigm for data handling with privacy preserved in the healthcare ecosystems supported by 5G. It collects advancements in quantum cryptography, Quantum Key Distribution (QKD), federated learning, and blockchain technology to explore how the combining of such technologies will enhance the confidentiality, interoperability, and real-time decision-making of the data. The review arranges and compares the state-of-theart research of 2019-2025, in which the trends, architectures, and security protocols that link classical and quantum methods in healthcare analytics become visible. It also addresses the main hurdles like quantum hardware limitations, interoperability gaps, and the tuning of federated models, and it indicates potential future research paths for the development of healthcare infrastructure that is scalable, reliable, and quantum-resistant. In conclusion, this paper gives a thorough understanding of how quantum intelligence can alter the data security and privacy landscape in 5G-enabled smart healthcare systems.
This book explores the intersection of artificial intelligence (AI) and quantum computing, focusing on the urgent need to secure machine learning systems in the face of emerging quantum threats. As quantum computers advance, they expose vulnerabilities in classical cryptographic methods, potentially undermining data integrity, privacy, and trust in AI-driven applications. To address these challenges, this study introduces the concept of post-quantum AI—a framework for integrating quantum-resistant cryptographic algorithms, Anomaly detection mechanisms, and Resilient machine learning architectures. This book makes three core contributions: it motivates a quantum-era threat model for machine learning (ii) it maps a migration path to standardised post-quantum cryptography Crypto-agile architectures (iii) it presents a defence-in-depth blueprint across the data → training → inference lifecycle that integrates privacy-preserving learning Governance. This work explicitly highlights key contributions, including proposed frameworks, algorithms, and case studies. Future research directions are also outlined to guide continued exploration in this emergent field. The final candidate algorithms from the NIST PQC standardisation process (NIST, 2022–2023) further strengthen this discussion. Key themes include the foundations of quantum mechanics relevant to computation, the fundamental differences between classical and quantum computing, and the transformative potential of quantum algorithms for optimisation, pattern recognition, and predictive analytics. The book highlights case studies spanning drug discovery, finance, mobile networks, and supply chain optimisation, illustrating how quantum-enhanced AI can revolutionise industry while simultaneously raising complex security and ethical concerns. A central focus is the development and deployment of post-quantum cryptography (PQC)), such as lattice-based and hash-based algorithms, to safeguard AI models against adversarial and quantum-accelerated attacks. The discussion extends to adversarial machine learning, explainable AI (XAI), and hybrid classical–quantum systems as strategies for strengthening resilience. The ethical, legal, and regulatory dimensions of post-quantum AI are also examined, emphasising fairness, transparency, accountability, and international cooperation. By combining technical innovations with responsible governance, the book advocates for building trustworthy AI systems that remain robust in the quantum era. Future work includes post-quantum cryptography (PQC) performance benchmarking in ML pipelines Patterns for crypto-agile key management, Assurance methods for privacy-preserving Federated learning as standards, and Implementations that are mature.
As artificial intelligence (AI) systems increasingly underpin critical applications in healthcare, finance, defense, and infrastructure, ensuring their security has become paramount. However, the rapid advancement of quantum computing poses a significant threat to classical cryptographic schemes that currently safeguard these systems. Quantum algorithms such as Shor’s and Grover’s threaten to undermine the confidentiality, integrity, and authentication of data and models that AI systems depend on. In this context, Post-Quantum Cryptography (PQC) has emerged as a crucial area of research aimed at developing cryptographic algorithms that are secure against both classical and quantum adversaries. This paper investigates the intersection of quantum computing threats and AI system vulnerabilities, highlighting how current security mechanisms may fail in a post-quantum era. We explore various categories of PQC—including lattice-based, hash-based, and multivariate polynomial cryptography—and evaluate their applicability to securing AI systems, particularly in scenarios such as model distribution, federated learning, data encryption, and communication in AI-enabled edge and IoT environments. Through analysis of emerging use cases and proposed architectures, the paper outlines how PQC can be integrated into the AI lifecycle to mitigate quantum-era threats. It also identifies key challenges such as computational overhead, integration complexity, and standardization gaps. The study concludes by outlining future research opportunities, including the development of efficient PQC protocols tailored to AI workloads, the creation of quantum-resilient AI pipelines, and the importance of interdisciplinary collaboration to secure AI’s future.
Because many modern healthcare security systems rely on traditional cryptographic primitives that are susceptible to quantum attacks, the quick development of quantum computing poses previously unheard-of risks. With the increasing use of wearable sensors, telemedicine, AI-driven diagnostics and distributed clinical databases, as well as the increasing digitisation of healthcare, the need for quantum-resilient protection becomes critical. The integration of quantum and post-quantum cryptography (PQC) as fundamental security measures for upcoming intelligent healthcare ecosystems is thoroughly examined in this chapter. We summarise recent developments in cryptographic standards, secure communication protocols, AI-enabled defence mechanisms and blockchain-based data integrity systems using a PRISMA-guided systematic review. With an emphasis on performance, scalability and resistance to Shor- and Grover-based attacks, we further develop a simulation framework to assess hybrid quantum, classical security architectures under adversarial quantum scenarios. Our results show that while hash-based signatures provide ultra-long-term integrity for genomic and archival medical data, lattice-based PQC schemes, in particular CRYSTALS-Kyber and Dilithium, show strong suitability for electronic health record (EHR) encryption, secure IoMT communication and federated learning aggregation. A unified quantum-resilient architecture incorporating PQC, blockchain consensus, AI-based anomaly detection and quantum key distribution (QKD) is one of the theoretical contributions. In summary, we highlight the potential of quantum homomorphic AI, neuromorphic cryptosystems, quantum biometrics and adaptive threat intelligence to develop a safe, reliable and scalable paradigm for quantum-ready healthcare.
The rapid evolution of healthcare systems, coupled with advancements in the Internet of Medical Things (IoMT), has introduced unprecedented security and privacy challenges. Traditional security solutions fail to address emerging threats posed by quantum computing and AI-driven cyberattacks. This paper proposes an Integrative Quantum Blockchain and AI-Driven Security Framework for Healthcare Systems, incorporating Quantum Key Distribution (QKD), Post-Quantum Cryptography (PQC), and AI-powered threat detection. The proposed framework enhances data integrity, authentication, and resistance against cyber threats, while optimizing energy efficiency. This paper presents the architecture, security benefits, performance analysis, and future research directions for integrating quantum blockchain and AI in healthcare security.
The increasing digitalization of the healthcare system, driven by electronic health records, medical IoT devices, and AI-aided diagnostics, has significantly expanded the attack surface for cyber threats. Although current cryptographic protocols provide sufficient security against classical adversaries, new developments in quantum computing threaten the confidentiality, integrity, and availability of medical information as never before. Shor’s algorithm could break widely used asymmetric encryption systems, and Grover’s algorithm weakens symmetric ones, which could render modern healthcare security systems obsolete. This review provides an in-depth and organized analysis of the evolving quantum threat environment in healthcare ecosystems and of the need to move toward quantum-resilient security architectures. It synthesizes the existing literature on post-quantum cryptography (PQC) and identifies lattice-based, hash-based, multivariate, and code-based algorithms as potentially viable long-term replacements. Moreover, it examines the cybersecurity issues that quantum-enabled attacks pose to medical IoT systems, clinical decision-support systems, wearable sensors, telemedicine systems, and cloud-based health information exchanges. The review also covers hybrid quantum-classical defenses, quantum key distribution (QKD), quantum random number generation (QRNG), and blockchain-based trust systems that aim to make systems resilient to advanced persistent quantum attacks. This paper identifies key gaps and outlines future research directions for developing quantum-secure medical systems. The results highlight the importance of early adoption of PQC standards and a layered cyber defense approach to protect patient privacy, ensure regulatory compliance, and retain trust in next-generation digital medical data communication.