  Quantum algorithms are emerging tools in the design of functional materials
due to their powerful solution space search capability. How to balance the high
price of quantum computing resources and the growing computing needs has become
an urgent problem to be solved. We propose a novel optimization strategy based
on an active learning scheme that combines the improved Quantum Genetic
Algorithm (QGA) with machine learning surrogate model regression. Using Random
Forests as the surrogate model circumvents the time-consuming physical modeling
or experiments, thereby improving the optimization efficiency. QGA, a genetic
algorithm embedded with quantum mechanics, combines the advantages of quantum
computing and genetic algorithms, enabling faster and more robust convergence
to the optimum. Using the design of planar multilayer photonic structures for
transparent radiative cooling as a testbed, we show superiority of our
algorithm over the classical genetic algorithm (CGA). Additionally, we show the
precision advantage of the RF model as a flexible surrogate model, which
relaxes the constraints on the type of surrogate model that can be used in
other quantum computing optimization algorithms (e.g., quantum annealing needs
Ising model as a surrogate).
