This study describes a hybrid quantum-classical computational approach for designing synthesizable deuterated Alq3 emitters possessing desirable emission quantum efficiencies (QEs). This design process has been performed on the tris(8-hydroxyquinolinato) ligands typically bound to aluminum in Alq3 and involves optimization of the hydrogen/deuterium (H/D) ratios in these molecules for the discovery of optimally deuterated high-efficiency OLED emitters. 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 can correctly 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, while the application of the readout error mitigation technique only improves probabilities to 0.90 and 0.084. We have developed the Recursive Probabilistic Variable Elimination (RPVE) method for simulations on noisy devices that recursively eliminates qubits depending on the probability that each qubit possesses a binary value and have demonstrated that RPVE improves the accuracies of VQE and QAOA for predictions of optimal solutions with probabilities of 0.97 on a quantum device.