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Quantum annealing was originally proposed as an approach for solvingcombinatorial optimisation problems using quantum effects. D-Wave Systems hasreleased a production model of quantum annealing hardware. However, theinherent noise and various environmental factors in the hardware hamper thedetermination of optimal solutions. In addition, the freezing effect in regionswith weak quantum fluctuations generates outputs approximately following aGibbs--Boltzmann distribution at an extremely low temperature. Thus, a quantumannealer may also serve as a fast sampler for the Ising spin-glass problem, andseveral studies have investigated Boltzmann machine learning using a quantumannealer. Previous developments have focused on comparing the performance inthe standard distance of the resulting distributions between conventionalmethods in classical computers and sampling by a quantum annealer. In thisstudy, we focused on the performance of a quantum annealer as a generativemodel. To evaluate its performance, we prepared a discriminator given by aneural network trained on an a priori dataset. The evaluation results show ahigher performance of quantum annealing compared with the classical approachfor Boltzmann machine learning.

Assessment of image generation by quantum annealer