A Quantum-Classical Method Applied to Material Design: Photochromic Materials Optimization for Photopharmacology Applications

Abstract

The integration of quantum chemistry, machine learning, and optimization calculations is expected to accelerate materials discovery by making large chemical spaces amenable to computational study, a challenging task for classical computers. In this study, we develop a quantum-classical computing scheme involving the computational-basis variational quantum deflation (cVQD) method for calculating the excited states of a general classical Hamiltonian, such as an Ising Hamiltonian. We apply this scheme to the practical use case of generating photochromic diarylethene (DAE) derivatives for photopharmacology applications. Using a dataset of 384 DAE derivatives from quantum chemistry calculation results, we show that machine learning can accurately predict the wavelength of maximum absorbance, [Formula: see text], of 4,096 DAE derivatives. After screening over 4,096 molecules using the computing scheme, we identified 5 DAE candidates that have important applications in photopharmacology. In detail, a 12-qubit cVQD calculation provides the ground state and 4 excited states of an Ising Hamiltonian corresponding to DAE candidates possessing large [Formula: see text]. On a quantum simulator, results are found to be in excellent agreement with those obtained by an exact eigensolver. Utilizing error suppression and mitigation techniques, cVQD on a real quantum device produces results with accuracy comparable to calculations on the simulator. Finally, we show that quantum chemistry calculations for the DAE candidates provides a path to achieving large [Formula: see text] and oscillator strengths by means of the molecular engineering of DAE derivatives.