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Hussain, Tarak, Virdee, Bal Singh, Alam, Irfan, Nagamani, Sikhinam, Nujumudeen, Faizal, Khan, Jalaluddin, Khanna, Ashish, Phanindra, K. and Fayaz, Mohsin (2026) Quantum-inspired variational optimization of neuromorphic self-supervised heterogeneous graph transformers for multimodal Parkinson’s disease representation learning. Discover Computing, 29(1) (791). pp. 1-49. ISSN 2948-2992
Early diagnosis of Parkinson’s disease (PD) remains challenging due to the heterogeneous nature of multimodal clinical data, limited annotated datasets, suboptimal multimodal fusion strategies, and the absence of uncertainty-aware decision-making in existing deep learning models. This paper proposes NeuroQ-MGST, a Neuromorphic Self-Supervised Multimodal Graph Transformer with Quantum-Inspired Variational Optimization, for robust and interpretable PD diagnosis. The proposed framework integrates a Spiking Neural Network (SNN) encoder to capture temporally sparse modality-specific representations, a self-supervised contrastive and masked reconstruction objective to exploit unlabeled data, and a Dynamic Heterogeneous Graph Construction (DHGC) with a Hierarchical Multimodal Graph Transformer (HMGT) to model complex cross-modal interactions among speech, gait, neuroimaging, and clinical biomarkers. To improve optimization in highly non-convex multimodal learning spaces, a Quantum-Inspired Variational Optimization (QIVO) module performs structured global representation refinement, while a Bayesian Monte Carlo Dropout layer provides calibrated predictive uncertainty for confidence-aware clinical decision support. Experimental evaluation on the PMMD 2.0 and UCI Parkinson’s Disease datasets demonstrates the effectiveness of the proposed framework, achieving 98.7% accuracy and 98.9% AUC-ROC, outperforming recent state-of-the-art multimodal approaches. Ablation studies validate the contribution of each architectural component, and cross-cohort evaluation on an independent European dataset achieves 95.8% zero-shot accuracy, confirming strong generalization capability. The proposed NeuroQ-MGST framework provides an accurate, interpretable, and uncertainty-aware solution for next-generation AI-assisted Parkinson’s disease diagnosis.
Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0.
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