Abstract 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.
Abstract 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.
Resting-state functional connectivity is widely used in machine learning-based classification of neuropsychiatric disorders. Quantum machine learning models have been proposed for functional connectivity data, but few studies have evaluated them under leave-site-out validation or against matched classical models. We compared six quantum model families with classical ones using identical correlation-derived features across four public cohorts (4,308 subjects). Leave-site-out validation was used for the Autism Brain Imaging Data Exchange (ABIDE I), ADHD-200 (attention-deficit/hyperactivity disorder), and REST-meta-MDD (major depressive disorder) cohorts, while the UCLA Consortium for Neuropsychiatric Phenomics (UCLA-CNP) was evaluated using stratified 10-fold cross-validation. The quantum kernel was statistically indistinguishable from a matched classical kernel across all four cohorts, with the average accuracy difference within 1.4 percentage points at 95% confidence. On ABIDE I, a hybrid quantum convolutional network achieved similar accuracy to its classical counterpart (0.645 vs. 0.641), while a trainable quantum kernel exceeded its classical counterpart by 0.064 in area under the curve (AUC). Quantum convolutional networks also achieved higher AUC than their matched classical networks on this cohort and showed smaller train-test gaps on all four cohorts, without an accuracy gain. Overall, most quantum models matched their classical counterparts, but none consistently outperformed them. This study, among the first quantum evaluations of ABIDE I under leave-site-out validation, provides a feature-matched benchmark of quantum and classical models comprising 284 matched comparisons. We further analyse fidelity-based graph construction and account for the observed quantum-classical parity through effective-dimension and eigenspectrum analyses.
Advances in neuroimaging and genomics have enabled unprecedented insights into the biological underpinnings of neuropsychiatric disorders; however, integrating heterogeneous, high-dimensional datasets remains a significant analytical challenge. This paper explores quantum neuroinformatics for multi-modal brain imaging, focusing on the integration of functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and genomic data to model complex neuropsychiatric phenotypes. By leveraging hybrid quantum–classical machine learning frameworks, the study examines how quantum-enhanced feature mapping, probabilistic modeling, and high-dimensional optimization can improve the fusion and interpretation of multimodal datasets. The analysis evaluates the potential of quantum algorithms to capture nonlinear interactions across neural, temporal, and genetic domains, while also addressing computational and hardware limitations associated with the Noisy Intermediate-Scale Quantum (NISQ) era. The findings suggest that quantum neuroinformatics may offer a novel pathway for advancing precision psychiatry by enabling more accurate phenotypic classification, biomarker discovery, and personalized treatment strategies.
Advances in neuroimaging and genomics have enabled unprecedented insights into the biological underpinnings of neuropsychiatric disorders; however, integrating heterogeneous, high-dimensional datasets remains a significant analytical challenge. This paper explores quantum neuroinformatics for multi-modal brain imaging, focusing on the integration of functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and genomic data to model complex neuropsychiatric phenotypes. By leveraging hybrid quantum–classical machine learning frameworks, the study examines how quantum-enhanced feature mapping, probabilistic modeling, and high-dimensional optimization can improve the fusion and interpretation of multimodal datasets. The analysis evaluates the potential of quantum algorithms to capture nonlinear interactions across neural, temporal, and genetic domains, while also addressing computational and hardware limitations associated with the Noisy Intermediate-Scale Quantum (NISQ) era. The findings suggest that quantum neuroinformatics may offer a novel pathway for advancing precision psychiatry by enabling more accurate phenotypic classification, biomarker discovery, and personalized treatment strategies.
This paper introduces CompressedMediQ, a novel hybrid quantum-classical machine learning pipeline specifically developed to address the computational challenges associated with high-dimensional multi-class neuroimaging data analysis. Standard neuroimaging datasets, such as large-scale MRI data from the Alzheimer’s Disease Neuroimaging Initiative and Neuroimaging in Frontotemporal Dementia, present significant hurdles due to their vast size and complexity. CompressedMediQ integrates classical HPC nodes for advanced MRI pre-processing and Convolutional Neural Network (CNN)-PCA-based feature extraction and reduction, addressing the limited-qubit availability for quantum data encoding in the NISQ era. This is followed by Quantum Support Vector Machine (QSVM) classification. By utilizing quantum kernel methods, the pipeline optimizes feature mapping and classification, enhancing data separability and outperforming traditional neuroimaging analysis techniques. Experimental results highlight the pipeline’s superior accuracy in dementia staging, validating the practical use of quantum machine learning in clinical diagnostics. Despite the limitations of NISQ devices, this proof-of-concept demonstrates the transformative potential of quantum-enhanced learning, paving the way for scalable and precise diagnostic tools in healthcare and signal processing.
The prompt detection of neurodegenerative diseases (NDDs) like Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI), and Parkinson's Disease (PD) is essential yet difficult. This research proposes a hybrid quantum-classical machine learning methodology to categorize NDD using multimodal brain imaging data.ADNI and PPMI datasets' MRI, PET, and fMRI images were preprocessing by Python-based tools such as Scikit-image, SimpleITK, and FreeSurfer via Nipype. Major steps were denoising, registration, normalization, segmentation, and extraction of features from structural and functional imaging. Training was done with a Quantum Support Vector Machine (QSVM) using Qiskit's ZZFeatureMap and QuantumKernel and compared with a conventional SVM. Performance measures-precision, recall, F1-score, class accuracy, AUC, and 10-fold cross-validation-were calculated. QSVM performed better than the standard SVM on all metrics, with 91.25% overall accuracy and macro F1-score of 0.913 as opposed to 87.75% and 0.879 for the SVM. The results validate QSVM's advantage in handling complex, high-dimensional neuroimaging data and its relevance to aiding clinical diagnosis. Keywords: Quantum Machine Learning, Neuroimaging, QSVM, Alzheimer’s Disease, Parkinson’s Disease, Multimodal Classification
Brain imaging is a critical tool in mental health diagnostics, particularly for detecting anomalies associated with behavioral disorders, anxiety, depression, and neuropsychiatric conditions. Traditional deep learning approaches, such as Convolutional Neural Networks (CNNs), have shown promise in analyzing functional Magnetic Resonance Imaging (fMRI), but they often suffer from computational limitations, feature extraction inefficiencies, and generalization issues. Quantum Machine Learning (QML) offers a transformative solution by enhancing feature representation, improving classification accuracy, and exploiting the power of quantum computation for large-scale medical imaging datasets. In this paper, we explored the potential of quantum models for detecting anomalies in brain imaging. Using the UCLA Consortium for Neuropsychiatric Phenomics dataset, we compared quantum models, including the Variational Quantum Classifier (VQC), Quantum Convolutional Neural Network (QCNN), and Quantum Support Vector Machine (QSVM), with classical models like CNNs and the Support Vector Machine (SVM). Our results revealed that QSVM and VQC outperformed classical models, both achieving 92% accuracy and perfect recall (100%), making them highly effective at identifying positive cases-a critical requirement in clinical diagnostics. QCNN, while less accurate at 74%, still showed strong precision (90%) and an F1score of 85%, highlighting its potential where avoiding false positives is key. Classical models, in comparison, performed moderately well: SVM reached 87% accuracy and CNN achieved 83%, but both had lower precision and F 1 -scores, suggesting less consistent performance. These findings suggest that quantum models, especially VQC and QSVM, offer a promising advantage in neuroimaging-based mental health diagnostics.