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

TL;DR

This paper presents a hybrid quantum-classical approach combining quantum simulations, machine learning, and optimization to accelerate the discovery of photochromic materials, demonstrating promising results for photopharmacology applications.

Contribution

It introduces a novel combined quantum-classical scheme using cVQD for excited state calculations and applies it to design photochromic DAE derivatives with optimized properties.

Findings

Quantum chemistry and machine learning accurately predict absorption wavelengths.

Quantum simulations on a 12-qubit device yield reliable excited state data.

The approach enables efficient exploration of large chemical spaces for material discovery.

Abstract

Integration of quantum chemistry simulations, machine learning techniques, and optimization calculations is expected to accelerate material discovery by making large chemical spaces amenable to computational study; a challenging task for classical computers. In this work, we develop a combined quantum-classical computing scheme involving the computational-basis Variational Quantum Deflation (cVQD) method for calculating excited states of a general classical Hamiltonian, such as Ising Hamiltonian. We apply this scheme to the practical use case of generating photochromic diarylethene (DAE) derivatives for photopharmacology applications. Using a data set of 384 DAE derivatives quantum chemistry calculation results, we show that a factorization-machine-based model can construct an Ising Hamiltonian to accurately predict the wavelength of maximum absorbance of the derivatives, $\lambda_{\rm max}$, for a larger set of 4096 DAE derivatives. A 12-qubit cVQD calculation for the constructed Ising Hamiltonian provides the ground and first four excited states corresponding to five DAE candidates possessing large $\lambda_{\rm max}$. 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 the ideal calculations on a simulator. Finally, we show that quantum chemistry calculations for the five DAE candidates provides a path to achieving large $\lambda_{\rm max}$ and oscillator strengths by molecular engineering of DAE derivatives. These findings pave the way for future work on applying hybrid quantum-classical approaches to large system optimization and the discovery of novel materials.