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arXiv AI By Utsav Poudel, Anuj Nepal, Vyas Viswanath, Subramaniyaswamy Vairavasundaram

Quantum Versus Classical Machine Learning for Neuropsychiatric Disorders: A Feature-Matched Multi-Cohort Evaluation

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The study compared six families of quantum machine learning models with classical counterparts using identical correlation-derived features across four large neuroimaging cohorts. Across all cohorts, quantum kernels performed statistically indistinguishable from matched classical kernels, with average accuracy differences within 1.4 percentage points. While a hybrid quantum convolutional network matched classical accuracy and a trainable quantum kernel slightly improved AUC on ABIDE I, overall quantum models did not consistently outperform classical ones, providing a feature‑matched benchmark of 284 comparisons.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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