Metastructures of titanium nitride (TiN), a plasmonic refractory material, can potentially achieve high solar absorptance while operating at elevated temperatures, but the design has been driven by expert intuition. Here, we design a high-performance solar absorber based on TiN metastructures using quantum computing-assisted optimization. The optimization scheme includes machine learning, quantum annealing, and optical simulation in an iterative cycle. It designs an optimal structure with solar absorptance > 95% within 40 h, much faster than an exhaustive search. Analysis of electric field distributions demonstrates that combined effects of Fabry-Perot interferences and surface plasmonic resonances contribute to the broadband high absorption efficiency of the optimally designed metastructure. The designed absorber may exhibit great potential for solar energy harvesting applications, and the optimization scheme can be applied to the design of other complex functional materials.
Keywords: machine learning; metastructure; quantum computing; solar absorber; thermophotovoltaic.