Assessment of image generation by quantum annealer

Abstract

Quantum annealing was originally proposed as an approach for solving combinatorial optimization 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 from a different aspect. 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 annealer compared with the classical approach for Boltzmann machine learning in training of the generative model. However the generation of the data suffers from the remanent quantum fluctuation in the quantum annealer. The quality of the generated images from the quantum annealer gets worse than the ideal case of the quantum annealing and the classical Monte-Carlo sampling.

Efficient low temperature Monte Carlo sampling using quantum annealing

“…degenerate states is the introduction of parallel tempering with isoenergetic cluster updates in Monte Carlo methods 16 or the combination with simulated annealing on a quantum annealer 17,18 . We mention that Boltzmann machines, which can serve as a link between machine learning and statistical thermodynamics 19 , are investigated in the context of QA [20][21][22][23] from a computer science perspective, but to the best of our knowledge, the direct application of QA for classical finite temperature modeling for statistical physics and materials science has not yet been accomplished and is the subject of the present paper.…”

Travel time optimization on multi-AGV routing by reverse annealing

“…The quantum effect on the case with multiple optimal solutions has also been discussed 35 , 36 . Further, applications of quantum annealing for machine learning for solving optimization problems have been reported 37 – 42 .…”

Comparing the effects of Boltzmann machines as associative memory in Generative Adversarial Networks between classical and quantum sampling

“…The application of the quantum annealer to combinatorial optimization problems has been widely studied. [19][20][21][22][23][24][25][26][27][28][29][30][31] The quantum effect on the degenerate ground state has also been investigated. 32,33) Several studies have reported sampling applications of quantum annealing to machine learning.…”