  Computational quantum mechanics based molecular and materials design
campaigns consume increasingly more high-performance compute resources, making
improved job scheduling efficiency desirable in order to reduce carbon
footprint or wasteful spending. We introduce quantum machine learning (QML)
models of the computational cost of common quantum chemistry tasks. For 2D
non-linear toy systems, single point, geometry optimization, and transition
state calculations the out of sample prediction error of QML models of wall
times decays systematically with training set size. We present numerical
evidence for a toy system containing two functions and three commonly used
optimizer and for thousands of organic molecular systems including closed and
open shell equilibrium structures, as well as transition states. Levels of
electronic structure theory considered include B3LYP/def2-TZVP, MP2/6-311G(d),
local CCSD(T)/VTZ-F12, CASSCF/VDZ-F12, and MRCISD+Q-F12/VDZ-F12. In comparison
to conventional indiscriminate job treatment, QML based wall time predictions
significantly improve job scheduling efficiency for all tasks after training on
just thousands of molecules. Resulting reductions in CPU time overhead range
from 10% to 90%.
