  The Boltzmann machine is one of the various applications using quantum
annealer. We propose an application of the Boltzmann machine to the kernel
matrix used in various machine-learning techniques. We focus on the fact that
shift-invariant kernel functions can be expressed in terms of the expected
value of a spectral distribution by the Fourier transformation. Using this
transformation, random Fourier feature (RFF) samples the frequencies and
approximates the kernel function. In this paper, furthermore, we propose a
method to obtain a spectral distribution suitable for the data using a
Boltzmann machine. As a result, we show that the prediction accuracy is
comparable to that of the method using the Gaussian distribution. We also show
that it is possible to create a spectral distribution that could not be
feasible with the Gaussian distribution.
