Using machine learning to optimize atomistic configurations, Ju et al. demonstrated that the configurations of silicon and germanium atoms in a lattice could be rapidly explored by means of Bayesian optimization to maximize or minimize phonon transport. Bayesian optimization is a method for optimizing black-box functions, and is characterized by the use of a Gaussian process as a surrogate model. Following the work of Ju et al., many structure search problems aimed at atomistic configurations have been solved using Bayesian optimization. However, Bayesian optimization has a problem in that when the number of candidates causes a combinatorial explosion, the time required for the selection of promising candidates increases exponentially because acquisition function calculations are required for all candidates. To overcome this limitation, Kitai et al. proposed a black-box optimization (BBO) algorithm using a quantum annealer known as the factorization machines with quantum annealing (FMQA) algorithm. In the FMQA algorithm, the factorization machine (FM) model is adopted as a surrogate model.