LATIDIA · Investigación

Aprendizaje automático cuántico versus clásico para trastornos neuropsiquiátricos: una evaluación de múltiples cohortes con características coincidentes

arXiv: 2610.21344v1Tipo de anuncio: nuevo Resumen: La conectividad funcional en estado de reposo se utiliza ampliamente en la clasificación de trastornos neuropsiquiátricos basada en el aprendizaje automático. Los modelos de aprendizaje automático cuántico han sido propo

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arXiv:2610.21344v1 Announce Type: new Abstract: 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