  Following the recent development of quantum machine learning techniques, the
literature has reported several quantum machine learning algorithms for disease
detection. This study explores the application of a hybrid quantum-classical
algorithm for classifying region-of-interest time-series data obtained from
resting-state functional magnetic resonance imaging in patients with
early-stage cognitive impairment based on the importance of cognitive decline
for dementia or aging. Classical one-dimensional convolutional layers are used
together with quantum convolutional neural networks in our hybrid algorithm. In
the classical simulation, the proposed hybrid algorithms showed higher balanced
accuracies than classical convolutional neural networks under the similar
training conditions. Moreover, a total of nine brain regions (left precentral
gyrus, right superior temporal gyrus, left rolandic operculum, right rolandic
operculum, left parahippocampus, right hippocampus, left medial frontal gyrus,
right cerebellum crus, and cerebellar vermis) among 116 brain regions were
found to be relatively effective brain regions for the classification based on
the model performances. The associations of the selected nine regions with
cognitive decline, as found in previous studies, were additionally validated
through seed-based functional connectivity analysis. We confirmed both the
improvement of model performance with the quantum convolutional neural network
and neuroscientific validities of brain regions from our hybrid
quantum-classical model.
