A Quantum Edge Federated Graph Transformer for Generative and Causal Digital Twin Healthcare

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.