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.
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.
Abstract Healthcare organizations have a high volume of sensitive data and traditional technologies have limited storage capacity and computational resources. The prospect of sharing healthcare data for machine learning is more arduous due to firm regulations related to patient privacy. The balanced protection of confidentiality, integrity, and availability of healthcare data, has become a major concern beyond classical data security considerations. In recent years, federated learning offers a solution to accelerate distributed machine learning addressing concerns related to data privacy and governance. Currently, the blend of quantum computing and machine learning has experienced significant attention from academic institutions and research communities. Quantum computers have shown the potential to bring huge benefits to the healthcare sector through efficient distributed training across several quantum nodes. The ultimate objective of this work is to develop a quantum federated learning framework (QFL) to tackle the optimization, security, and privacy challenges in the healthcare and clinical industries for medical imaging tasks. In this work, we proposed federated quantum convolutional neural networks (QCNNs) with distributed training across edge devices. To demonstrate the feasibility of the proposed QFL framework, we performed extensive experiments on medical datasets (Pneumonia MNIST, and CT-kidney disease analysis), which are non-independently and non-identically partitioned among the healthcare institutions/clients. The quantum federated global model maintained a high classification testing accuracy and generalizability and outperformed the locally train clients regardless of how unbalanced the medical data is distributed among the clients. The global model achieved the best performance as compared to local clients in terms of the area under curve of the receiver operating characteristic curve (AUCROC) (0.953) and an average of (0.98) on all classes for predicting outcomes on pneumonia and CT-kidney datasets, respectively. Moreover, the client selection mechanism is proposed to reduce the computation overhead at each communication round, which effectively improves the convergence rate. The proposed quantum federated learning framework is validated and assessed via large-scale simulations. Based on our results from numerical simulations, the deployment of distributed and secure quantum machine learning algorithms for enabling scalable and privacy-preserving intelligent healthcare applications would be extremely valuable.
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 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.
Healthcare organizations have a high volume of sensitive data and traditional technologies have limited storage capacity and computational resources. The prospect of sharing healthcare data for machine learning is more arduous due to government regulations related to patient privacy. In recent years, federated learning has offered a solution to accelerate distributed machine learning addressing concerns related to data privacy and governance. Currently, the blend of quantum computing and machine learning has experienced significant attention from academic institutions and research communities. The ultimate objective of this work is to develop a federated quantum machine learning framework (FQML) to tackle the optimization, security, and privacy challenges in the healthcare industry for medical imaging tasks. In this work, we propose a new methodology based on federated quantum convolutional neural networks (QCNNs) with distributed training across edge devices. To demonstrate the feasibility of the proposed FQML framework, we performed extensive experiments on two benchmark medical datasets (Pneumonia MNIST, and CT kidney disease analysis), which are non-independently and non-identically partitioned among the healthcare institutions/clients. The proposed framework is validated and assessed via large-scale simulations. Based on our results, the quantum simulation experiments achieve performance levels on par with well-known classical CNN models, 86.3% accuracy on the pneumonia dataset and 92.8% on the CT-kidney dataset, while requiring fewer model parameters and consuming less data. Moreover, a client selection mechanism is proposed to reduce the computation overhead at each communication round, which effectively improves the convergence rate.
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.
Healthcare 5.0 combines artificial intelligence, the Internet of Medical Things, edge and cloud computing, and digital twins to support safer, more personal, and more efficient care. Interest in quantum machine learning is growing in this space, but the current evidence is fragmented and hard to compare across studies. Many reports focus on small tasks, use weak or inconsistent baselines, and do not separate algorithmic gains from costs caused by data encoding, repeated circuit execution, and hardware noise. This survey addresses these gaps by providing a structured synthesis of Healthcare 5.0 applications and by offering a practical comparison between classical machine learning and quantum machine learning using criteria that matter for clinical use. We review representative use cases in medical imaging, electronic health record based prediction, remote monitoring, decision support, and digital twin enabled systems. We then analyze QML integration patterns that are realistic today, with emphasis on hybrid pipelines where classical models extract features and quantum components act as narrow decision or optimization modules. Finally, we consolidate the barriers that currently limit adoption, including encoding overhead, limited qubit counts and circuit depth, training instability and reproducibility, privacy and governance constraints, and the need for careful benchmarking and reporting. We conclude with guidance on evaluation and study design to support fair comparison, clearer clinical relevance, and better alignment with Healthcare 5.0 engineering requirements.