  Quantum annealing was originally proposed as an approach for solving
combinatorial optimisation problems using quantum effects. D-Wave Systems has
released a production model of quantum annealing hardware. However, the
inherent noise and various environmental factors in the hardware hamper the
determination of optimal solutions. In addition, the freezing effect in regions
with weak quantum fluctuations generates outputs approximately following a
Gibbs--Boltzmann distribution at an extremely low temperature. Thus, a quantum
annealer may also serve as a fast sampler for the Ising spin-glass problem, and
several studies have investigated Boltzmann machine learning using a quantum
annealer. Previous developments have focused on comparing the performance in
the standard distance of the resulting distributions between conventional
methods in classical computers and sampling by a quantum annealer. In this
study, we focused on the performance of a quantum annealer as a generative
model. To evaluate its performance, we prepared a discriminator given by a
neural network trained on an a priori dataset. The evaluation results show a
higher performance of quantum annealing compared with the classical approach
for Boltzmann machine learning.
