  The use of quantum annealers in black-box optimization to obtain the desired
properties of a product with a small number of trials has attracted attention.
However, the application of this technique to engineering design problems is
still limited. Here, we demonstrate the applicability of black-box optimization
with a quantum annealer to the design of electric circuit systems, focusing on
$\pi$-type noise filters as an example. We develop a framework that uses
quantum annealing to find the optimal location of electrical components and
conductor paths connecting the components, and confirm that the learning
process appropriately works over a number of trials to efficiently search for a
design with high performance. The results show the potential applicability of
quantum annealing to design problems of electric circuit systems.
