Assessment of image generation by quantum annealer

TL;DR

This paper evaluates the effectiveness of quantum annealers as generative models, demonstrating their superior performance over classical methods in Boltzmann machine learning tasks.

Contribution

It introduces a novel evaluation of quantum annealers as generative models, highlighting their advantages over classical approaches in specific learning tasks.

Findings

Quantum annealers outperform classical methods in Boltzmann machine learning.

The study demonstrates the potential of quantum annealers as fast samplers for probabilistic models.

Quantum annealing shows promise for generative modeling in machine learning applications.

Abstract

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.