  We introduce an electronic structure based representation for quantum machine
learning (QML) of electronic properties throughout chemical compound space. The
representation is constructed using computationally inexpensive ab initio
calculations and explicitly accounts for changes in the electronic structure.
We demonstrate the accuracy and flexibility of resulting QML models when
applied to property labels such as total potential energy, HOMO and LUMO
energies, ionization potential, and electron affinity, using as data sets for
training and testing entries from the QM7b, QM7b-T, QM9, and LIBE libraries.
For the latter, we also demonstrate the ability of this approach to account for
molecular species of different charge and spin multiplicity, resulting in QML
models that infer total potential energies based on geometry, charge, and spin
as input.
