  Super-resolution is a machine-learning technique in image processing which
generates high-resolution images from low-resolution images. Inspired by this
approach, we perform a numerical experiment of quantum machine learning, which
takes low-resolution (low plane-wave energy cutoff) one-particle molecular
wavefunctions in plane-wave basis as input and generates high-resolution (high
plane-wave energy cutoff) wavefunctions in fictitious one-dimensional systems,
and study the performance of different learning models. We show that the
trained models can generate wavefunctions having higher fidelity values with
respect to the ground-truth wavefunctions than a simple linear interpolation,
and the results can be improved both qualitatively and quantitatively by
including data-dependent information in the ansatz. On the other hand, the
accuracy of the current approach deteriorates for wavefunctions calculated in
electronic configurations not included in the training dataset. We also discuss
the generalization of this approach to many-body electron wavefunctions.
