  The role of response operators is well established in quantum mechanics. We
investigate their use for universal quantum machine learning models of response
properties in molecules. After introducing a theoretical basis, we present and
discuss numerical evidence based on measuring the potential energy's response
with respect to atomic displacement and to electric fields. Prediction errors
for corresponding properties, atomic forces and dipole moments, improve in a
systematic fashion with training set size and reach high accuracy for small
training sets. Prediction of normal modes and IR-spectra of some small
molecules demonstrates the usefulness of this approach for chemistry.
