  Molecular fingerprints are widely used for predicting chemical properties,
and selecting appropriate fingerprints is important. We generate new
fingerprints based on the assumption that a performance of prediction using a
more effective fingerprint is better. We generate effective interaction
fingerprints that are the product of multiple base fingerprints. It is
difficult to evaluate all combinations of interaction fingerprints because of
computational limitations. Against this problem, we transform a problem of
searching more effective interaction fingerprints into a quadratic
unconstrained binary optimization problem. In this study, we found effective
interaction fingerprints using QM9 dataset.
