  Since the introduction of the quantum neural network model, it has been
widely studied due to its strong expressive power and robustness to
overfitting. To date, the model has been evaluated primarily in classification
tasks, but its performance in practical multivariate regression problems has
not been thoroughly examined. In this study, the Auto-MPG data set (392 valid
data points, excluding missing data, on fuel efficiency for various vehicles)
was used to construct QNN models and investigate the effect of the size of the
training data on generalization performance. The results indicate that QNN is
particularly effective when the size of training data is small, suggesting that
it is especially suitable for small-data problems such as those encountered in
Materials Informatics.
