  Academic achievement is a critical measure of intellectual ability, prompting
extensive research into cognitive tasks as potential predictors. Neuroimaging
technologies, such as functional near-infrared spectroscopy (fNIRS), offer
insights into brain hemodynamics, allowing understanding of the link between
cognitive performance and academic achievement. Herein, we explored the
association between cognitive tasks and academic achievement by analyzing
prefrontal fNIRS signals. A novel quantum annealer (QA) feature selection
algorithm was applied to fNIRS data to identify cognitive tasks correlated with
CSAT scores. Twelve features (signal mean, median, variance, peak, number of
peaks, sum of peaks, slope, minimum, kurtosis, skewness, standard deviation,
and root mean square) were extracted from fNIRS signals at two time windows
(10- and 60-second) to compare results from various feature variable
conditions. The feature selection results from the QA-based and XGBoost
regressor algorithms were compared to validate the former's performance. In a
three-step validation process using multiple linear regression models,
correlation coefficients between the feature variables and the CSAT scores,
model fitness (adjusted R2), and model prediction error (RMSE) values were
calculated. The quantum annealer demonstrated comparable performance to
classical machine learning models, and specific cognitive tasks, including
verbal fluency, recognition, and the Corsi block tapping task, were correlated
with academic achievement. Group analyses revealed stronger associations
between Tower of London and N-back tasks with higher CSAT scores. Quantum
annealing algorithms have significant potential in feature selection using
fNIRS data, and represents a novel research approach. Future studies should
explore predictors of academic achievement and cognitive ability.
