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Integration of quantum chemistry simulations, machine learning techniques,and optimization calculations is expected to accelerate material discovery bymaking large chemical spaces amenable to computational study; a challengingtask for classical computers. In this work, we develop a combinedquantum-classical computing scheme involving the computational-basisVariational Quantum Deflation (cVQD) method for calculating excited states of ageneral classical Hamiltonian, such as Ising Hamiltonian. We apply this schemeto the practical use case of generating photochromic diarylethene (DAE)derivatives for photopharmacology applications. Using a data set of 384 DAEderivatives quantum chemistry calculation results, we show that afactorization-machine-based model can construct an Ising Hamiltonian toaccurately predict the wavelength of maximum absorbance of the derivatives,$\lambda_{\rm max}$, for a larger set of 4096 DAE derivatives. A 12-qubit cVQDcalculation for the constructed Ising Hamiltonian provides the ground and firstfour excited states corresponding to five DAE candidates possessing large$\lambda_{\rm max}$. On a quantum simulator, results are found to be inexcellent agreement with those obtained by an exact eigensolver. Utilizingerror suppression and mitigation techniques, cVQD on a real quantum deviceproduces results with accuracy comparable to the ideal calculations on asimulator. Finally, we show that quantum chemistry calculations for the fiveDAE candidates provides a path to achieving large $\lambda_{\rm max}$ andoscillator strengths by molecular engineering of DAE derivatives. Thesefindings pave the way for future work on applying hybrid quantum-classicalapproaches to large system optimization and the discovery of novel materials.

A combined quantum-classical method applied to material design:  optimization and discovery of photochromic materials for photopharmacology  applications