Resting-state functional connectivity is a common input for machine learning models that classify neuropsychiatric disorders. A new preprint on arXiv proposes to evaluate whether quantum machine learning models can improve on classical approaches for this task. The study is described as a feature-matched, multi-cohort evaluation, meaning the models are compared under matched features across several datasets.
The abstract notes that quantum machine learning models have been proposed for functional connectivity data, but few studies have—the sentence cuts off. As a result, the available text does not include the study's findings or conclusions. The preprint's title and opening indicate a systematic comparison, but the specific outcomes remain unavailable from the source.