  The combination of modern scientific computing with electronic structure
theory can lead to an unprecedented amount of data amenable to intelligent data
analysis for the identification of meaningful, novel, and predictive
structure-property relationships. Such relationships enable high-throughput
screening for relevant properties in an exponentially growing pool of virtual
compounds that are synthetically accessible. Here, we present a machine
learning (ML) model, trained on a data base of \textit{ab initio} calculation
results for thousands of organic molecules, that simultaneously predicts
multiple electronic ground- and excited-state properties. The properties
include atomization energy, polarizability, frontier orbital eigenvalues,
ionization potential, electron affinity, and excitation energies. The ML model
is based on a deep multi-task artificial neural network, exploiting underlying
correlations between various molecular properties. The input is identical to
\emph{ab initio} methods, \emph{i.e.} nuclear charges and Cartesian coordinates
of all atoms. For small organic molecules the accuracy of such a "Quantum
Machine" is similar, and sometimes superior, to modern quantum-chemical
methods---at negligible computational cost.
