Quantum Kernel Methods for Calibrated and Interpretable Neuroimaging Classification in Computational Psychiatry

Psychiatric disorders such as attention deficit hyperactivity disorder, schizophrenia, and bipolar disorder are notoriously difficult to distinguish on neuroimaging alone, owing to overlapping symptom profiles and heterogeneous neurobiological substrates. Conventional machine learning classifiers applied to structural MRI and electroencephalography data have shown modest discriminative performance and, more critically, tend to produce poorly calibrated probability estimates that limit their utility in clinical decision-making. We present a dual-pipeline framework in which quantum kernel support vector machines are applied independently to MRI-derived morphometric features and to EEG spectral representations, followed by rigorous post-hoc calibration and interpretability analyses. On the UCLA Consortium for Neuropsychiatric Phenomics dataset, a 4-qubit quantum kernel SVM achieved an AUC of 0.957 and a Matthews correlation coefficient of 0.679 for ADHD versus control discrimination, while a tuned classical radial basis function SVM performed at chance (AUC=0.497). For EEG-based emotion and cognitive state classification, a hybrid quantum-classical kernel outperformed both its pure quantum and classical counterparts (AUC=0.841 versus 0.824 and 0.812, respectively). Temperature scaling reduced expected calibration error by 85% for the MRI pipeline and by 49% for the EEG pipeline. Multimodal fusion of the two quantum kernel Gram matrices yielded an ECE of 0.0075, the lowest observed across all experimental configurations. Gradient-weighted class activation mapping and Shapley additive explanations recovered neurobiologically established signatures of ADHD and psychotic disorder in prefrontal, cingulate, and basal ganglia regions, lending biological credibility to the learned representations. Taken together, these results indicate that quantum kernel methods can confer genuine advantages over classical baselines in small-sample neuroimaging settings, and that the resulting models can be made reliable enough for clinical deployment through standard calibration procedures.