  This study describes a hybrid quantum-classical computational approach for
designing synthesizable deuterated $Alq_3$ emitters possessing desirable
emission quantum efficiencies (QEs). This design process has been performed on
the tris(8-hydroxyquinolinato) ligands typically bound to aluminum in $Alq_3$.
It involves a multi-pronged approach which first utilizes classical quantum
chemistry to predict the emission QEs of the $Alq_3$ ligands. These initial
results were then used as a machine learning dataset for a factorization
machine-based model which was applied to construct an Ising Hamiltonian to
predict emission quantum efficiencies on a classical computer. We show that
such a factorization machine-based approach can yield accurate property
predictions for all 64 deuterated $Alq_3$ emitters with 13 training values.
Moreover, another Ising Hamiltonian could be constructed by including synthetic
constraints which could be used to perform optimizations on a quantum simulator
and device using the variational quantum eigensolver (VQE) and quantum
approximate optimization algorithm (QAOA) to discover a molecule possessing the
optimal QE and synthetic cost. We observe that both VQE and QAOA calculations
can predict the optimal molecule with greater than 0.95 probability on quantum
simulators. These probabilities decrease to 0.83 and 0.075 for simulations with
VQE and QAOA, respectively, on a quantum device, but these can be improved to
0.90 and 0.084 by mitigating readout error. Application of a binary search
routine on quantum devices improves these results to a probability of 0.97 for
simulations involving VQE and QAOA.
