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.
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.
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.
Modern healthcare systems increasingly rely on artificial intelligence, deep learning, and medical imaging technologies for accurate disease diagnosis and clinical decision-making. However, conventional AI systems still face challenges related to interpretability, computational complexity, centralized data dependency, and patient privacy. This paper presents an Explainable Quantum Deep Learning Framework integrating Vision Transformers, Federated Intelligence, and Explainable Artificial Intelligence techniques for smart healthcare applications. The proposed framework combines quantum-inspired optimization, multimodal medical data analysis, transformer-based feature extraction, and decentralized federated learning to improve diagnostic accuracy, scalability, privacy preservation, and interpretability. Explainability mechanisms including SHAP and Grad-CAM are incorporated to enhance physician trust and clinical transparency. Experimental evaluation demonstrates superior performance in biomedical image classification and disease prediction compared with conventional deep learning models, providing a scalable solution for next-generation intelligent healthcare systems.
Smart healthcare aims to revolutionize medical services by integrating artificial intelligence (AI). The limitations of classical machine learning include privacy concerns that prevent direct data sharing among medical institutions, untimely updates, and long training times. To address these issues, this study proposes a digital twin-assisted quantum federated learning algorithm (DTQFL). By leveraging the 5G mobile network, digital twins (DT) of patients can be created instantly using data from various Internet of Medical Things (IoMT) devices and simultaneously reduce communication time in federated learning (FL) at the same time. DTQFL generates DT for patients with specific diseases, allowing for synchronous training and updating of the variational quantum neural network (VQNN) without disrupting the VQNN in the real world. This study utilized DTQFL to train its own personalized VQNN for each hospital, considering privacy security and training speed. Simultaneously, the personalized VQNN of each hospital was obtained through further local iterations of the final global parameters. The results indicate that DTQFL can train a good VQNN without collecting local data while achieving accuracy comparable to that of data-centralized algorithms. In addition, after personalized training, the VQNN can achieve higher accuracy than that without personalized training.
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.
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 proliferation of large-scale analytics across distributed edge–cloud environments has raised critical challenges related to data privacy, communication overhead, and computational efficiency. To address these issues, this paper proposes a Quantum-Guided Federated Transformer (QGFT) framework for privacy-preserving large-scale analytics, integrating quantum-inspired optimization with federated transformer learning. In the proposed model, a quantum-guided variational optimizer leverages superposition-driven exploration and entanglement-inspired correlation modeling to adaptively optimize transformer attention weights during decentralized training, without sharing raw data. Federated aggregation is enhanced using privacy-aware adaptive parameter fusion, enabling robust convergence under heterogeneous and non-IID client data distributions. Extensive evaluations conducted on large-scale benchmark datasets demonstrate that QGFT achieves a 3.8–6.4% improvement in analytics accuracy, a 28–35% reduction in communication overhead, and a 22–31% faster convergence rate compared to conventional federated transformer and Fed Avg-based approaches. Furthermore, the integration of quantum-guided optimization improves model generalization by 18.6% under high data heterogeneity. Differential privacy analysis confirms that the proposed framework maintains a privacy leakage probability below 10⁻⁶, satisfying stringent regulatory requirements. These results indicate that QGFT provides a scalable, secure, and efficient solution for next-generation privacy-preserving analytics in federated edge–cloud intelligence. The framework enhances the confidentiality of data during the collaborating process of different nodes while preserving the accuracy of the models across multiple resources. In addition, QGFT is adaptable to all types of edge devices, thus enabling intelligent distributed systems to use Privacy-Aware systems.