Quantum variational graph-driven neural framework for genomic-clinical integration in precision diagnosis.

Purpose: Integrating genomic and clinical data for precision diagnosis poses significant challenges due to high dimensionality, non-linear dependencies, and heterogeneity across data types. Existing machine learning and hybrid quantum-classical models often struggle to effectively integrate multimodal biomedical data in a way that is interpretable and scalable. To address these limitations, this research proposes a Quantum Variational Graph-Driven Neural (QVGDN) framework, designed to capture complex cross-modal interactions and relational patient structures with quantum-enhanced computing solutions. Methods: The QVGDN framework incorporates a quantum adaptive interference readout mechanism for modality-specific feature extraction, integrates genomic and clinical features through a variational encoding with cross-factorized attention fusion module, and performs relational diagnosis with a multi-omic quantum entangled kernel-based external graph neural network. The hyperparameters are fine-tuned using the Superb Fairy-wren Optimization Algorithm for convergence efficiency. Results: The experimental evaluation on two benchmark datasets shows that QVGDN achieves 99.12% accuracy and high performance consistently in noisy and limited data conditions. Compared with classical and hybrid quantum baseline models, QVGDN significantly improves diagnostic precision, sample efficiency, and relational interpretability. Conclusion: The proposed framework is highly suitable for deployment in clinical decision support systems where interpretability and computational efficiency are essential by offering a scalable solution amenable to real-time precision diagnostics.