  Complexity of materials designed by machine learning is currently limited by
the inefficiency of classical computers. We show how quantum annealing can be
incorporated into automated materials discovery and conduct a
proof-of-principle study on designing complex thermofunctional metamaterials
consisting of SiO2, SiC, and Poly(methyl methacrylate). Empirical computing
time of our quantum-classical hybrid algorithm involving a factorization
machine, a rigorous coupled wave analysis, and a D-Wave 2000Q quantum annealer
was insensitive to the problem size, while a classical counterpart experienced
rapid increase. Our method was used to design complex structures of wavelength
selective radiators showing much better concordance with the thermal
atmospheric transparency window in comparison to existing human-designed
alternatives. Our result shows that quantum annealing provides scientists
gigantic computational power that may change how materials are designed.
