The continuous growth of healthcare data requires robust architectures and solutions. Thanks to Quantum Federated Machine Learning (QFML), which combines quantum computing power, federated learning, and blockchain technology, that enhances decentralization and privacy-preserving benefits. However, the combination of these technologies brings a new level of complexity. This paper presents a systematic literature review on the security of QFML in blockchain for healthcare. Our review shows the limitations and the lack of adequate and robust solutions to address the current vulnerabilities. The review then paves the road for advancing the security of the QFML system on blockchain for healthcare applications.
The continuous growth of healthcare data requires robust architectures and solutions. Thanks to Quantum Federated Machine Learning (QFML), which combines quantum computing power, federated learning, and blockchain technology, that enhances decentralization and privacy-preserving benefits. However, the combination of these technologies brings a new level of complexity. This paper presents a systematic literature review on the security of QFML in blockchain for healthcare. Our review shows the limitations and the lack of adequate and robust solutions to address the current vulnerabilities. The review then paves the road for advancing the security of the QFML system on blockchain for healthcare applications.
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.
The increasing availability of electronic health records, medical imaging databases, and sensor-generated clinical datasets has boosted the need for privacy-preserving yet scalable analytical frameworks. In this regard, quantum computing and federated learning are becoming two focal points that may significantly shape future healthcare data processing. Quantum computing provides powerful tools for working with complex, high-dimensional optimization and learning problems, while federated learning facilitates collaborative model training without requiring the centralized exchange of sensitive information about patients. This review explores the integration of these two technologies and Quantum Split Federated Learning (QSFL) as a promising approach for secure and scalable co-operative health care analytics. The treatment then turns to discuss the quantum computation mathematical and algorithmic basics, and subsequently, quantum machine learning methods based on extremes of supervised, unsupervised, and semi-supervised modes. Special emphasis is placed on how QSFL can enable privacy-aware inter-institutional learning and enhance clinical decision support systems. It also surveys the applications for creating high-quality synthetic medical data sets for training, validation and data-sharing using quantum generative adversarial networks (qGANs). In addition to the technological opportunities this paper critically evaluates the practical challenges to real-world implementation, such as hardware limitations, communication costs, regulatory matters, and ethical considerations of a distributed medical learning system-based infrastructure and mechanism. In the end, the survey identifies important open research problems and provides suggestions for future research directions towards realizing quantum-enabled, privacy-preserving healthcare intelligence systems that are anticipated to usher in a new era of medical analytics.
Healthcare data management has undergone a transformation through digital advancements which emerged from clinical research databases, pharmaceutical supply chains, electronic health records (EHRs), and patient consent systems. Quantum computing systems that execute Shor's algorithm to break down RSA and ECC cryptographic systems will join the list of cyber threats which currently includes ransomware and insider attacks. The blockchain system provides a transparent decentralized ledger with permanent records that protects data integrity and traceability. Its ability to scale and defend against quantum attacks remains limited. The proposed multi-layered solution in this chapter combines blockchain technology with approximate quantum cloning and privacy-preserving machine learning (ML) to enhance healthcare data security, redundancy, and analytics capabilities. By addressing quantum threats and regulatory requirements, this framework fosters a resilient, patient-centric healthcare ecosystem, with future research focusing on scalability and quantum hardware integration.
The rapid digitalisation of healthcare has accelerated the adoption of telemedicine, Electronic Health Records (EHRs), and the Internet of Medical Things (IoMT), transforming healthcare delivery into a highly interconnected and patient-centric ecosystem. In response to growing concerns about data security, privacy, and interoperability, blockchain technology has emerged as a promising solution for its decentralization, immutability, auditability, and secure access control. However, many existing blockchain infrastructures rely on classical cryptographic primitives, including RSA- or elliptic-curve-based public-key mechanisms and cryptographic hash functions such as SHA-256, whose relevant security properties may be affected by sufficiently powerful quantum attacks. This review investigates the convergence of blockchain and quantum technologies to address emerging security threats in e-health systems. A structured literature review was conducted in accordance with the PRISMA 2020 guidelines using the IEEE Xplore, PubMed, ACM Digital Library, Google Scholar, and Crossref databases, covering studies published between January 2018 and June 2025. Following a systematic screening and eligibility-verification process, 57 relevant studies were selected and analyzed. The review evaluates quantum-resilient security mechanisms, including Quantum Key Distribution (QKD), Quantum Random Number Generation (QRNG), and NIST-standardized Post-Quantum Cryptography (PQC) algorithms specified in FIPS 203, FIPS 204, and FIPS 205. Based on the identified research gaps in the state of the art, this study also proposes a novel four-layer Quantum-Blockchain Security Architecture (QBSA) designed for secure healthcare environments. The analysis further reveals significant challenges associated with lightweight PQC deployment for IoMT devices, interoperability standardization, quantum hardware limitations, and regulatory compliance in cross-institutional healthcare systems. The findings highlight the necessity of integrating quantum-resilient cryptographic frameworks with blockchain infrastructures to support the development of secure, scalable, and patient-centric next-generation e-health ecosystems.
The rapid evolution of digital technologies has intensified the need for secure, transparent, and efficient digital ecosystems. Blockchain technology, known for its decentralized and tamper-resistant nature, has emerged as a promising solution for ensuring data integrity and trust. Simultaneously, Quantum Machine Learning (QML) is gaining attention for its potential to revolutionize computational capabilities by leveraging quantum computing principles. This paper explores the integration of blockchain and QML to design secure digital ecosystems capable of handling complex data processing and ensuring robust security. Blockchain provides a distributed ledger for secure data storage and transaction validation, while QML enhances analytical capabilities through advanced pattern recognition and optimization techniques. The proposed framework addresses key challenges such as data privacy, scalability, and computational efficiency. Additionally, the study examines the role of smart contracts in automating processes and ensuring compliance within decentralized environments. Security concerns, including quantum threats to classical cryptographic systems, are also discussed, along with potential solutions such as quantum-resistant cryptography. The findings indicate that the integration of blockchain and QML can significantly enhance the security, efficiency, and scalability of digital ecosystems, making them suitable for applications in finance, healthcare, and enterprise systems.
Quantum federated learning (QFL) is a combination of distributed quantum computing and federated machine learning, integrating the strengths of both to enable privacy-preserving decentralized learning with quantum-enhanced capabilities. It appears as a promising approach for addressing challenges in efficient and secure model training across distributed quantum systems. This paper presents a comprehensive survey on QFL, exploring its key concepts, fundamentals, applications, and emerging challenges in this rapidly developing field. Specifically, we begin with an introduction to the recent advancements of QFL, followed by discussion on its market opportunity and background knowledge. We then discuss the motivation behind the integration of quantum computing and federated learning, highlighting its working principle. Moreover, we review the fundamentals of QFL and its taxonomy. Particularly, we explore federation architecture, networking topology, communication schemes, optimization techniques, and security mechanisms within QFL frameworks. Furthermore, we investigate applications of QFL across several domains which include vehicular networks, healthcare networks, satellite networks, metaverse, and network security. Additionally, we analyze frameworks and platforms related to QFL, delving into its prototype implementations, and provide a detailed case study. Key insights and lessons learned from this review of QFL are also highlighted. We complete the survey by identifying current challenges and outlining potential avenues for future research in this rapidly advancing field.