  In materials informatics, searching for chemical materials with desired
properties is challenging due to the vastness of the chemical space. Moreover,
the high cost of evaluating properties necessitates a search with a few clues.
In practice, there is also a demand for proposing compositions that are easily
synthesizable. In the real world, such as in the exploration of chemical
materials, it is common to encounter problems targeting black-box objective
functions where formalizing the objective function in explicit form is
challenging, and the evaluation cost is high. In recent research, a Bayesian
optimization method has been proposed to formulate the quadratic unconstrained
binary optimization (QUBO) problem as a surrogate model for black-box objective
functions with discrete variables. Regarding this method, studies have been
conducted using the D-Wave quantum annealer to optimize the acquisition
function, which is based on the surrogate model and determines the next
exploration point for the black-box objective function. In this paper, we
address optimizing a black-box objective function containing discrete variables
in the context of actual chemical material exploration. In this optimization
problem, we demonstrate results obtaining parameters of the acquisition
function by sampling from a probability distribution with variance can explore
the solution space more extensively than in the case of no variance. As a
result, we found combinations of substituents in compositions with the desired
properties, which could only be discovered when we set an appropriate variance.
