Designing metamaterials with quantum annealing

Abstract

Along with the advances in manipulation method for atomistic and spectroscopic characteristics of materials, designing them with machine learning algorithms is increasingly common in recent years. That is because the designing is defined as black-box optimization, which is generally a difficult problem. Its difficulty grows exponentially in the number of variables and severely suffers the classical search algorithms. We combine a regression model called factorization machine with quantum annealing to propose a new quantum-classical hybrid algorithm and show how it can be incorporated into automated materials discovery. The quantum annealing greatly reduces the time for selection from the massive number of candidates. As a proof-of-principle work, we used the algorithm with an analytical method in computational electromagnetics called RCWA to design wavelength selective radiator. The resulting material showed much better concordance with the thermal atmospheric transparency window than existing human-designed alternatives. It indicates the further use of quantum annealing in real-world design problems.