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...

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Autores principales:	Takehito Sato, Masayuki Ohzeki, Kazuyuki Tanaka
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/01212a78e03d47b2b0983986af394b84
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spelling	oai:doaj.org-article:01212a78e03d47b2b0983986af394b842021-12-02T16:31:50ZAssessment of image generation by quantum annealer10.1038/s41598-021-92295-92045-2322https://doaj.org/article/01212a78e03d47b2b0983986af394b842021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92295-9https://doaj.org/toc/2045-2322Abstract 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.Takehito SatoMasayuki OhzekiKazuyuki TanakaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Takehito Sato Masayuki Ohzeki Kazuyuki Tanaka Assessment of image generation by quantum annealer
description	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.
format	article
author	Takehito Sato Masayuki Ohzeki Kazuyuki Tanaka
author_facet	Takehito Sato Masayuki Ohzeki Kazuyuki Tanaka
author_sort	Takehito Sato
title	Assessment of image generation by quantum annealer
title_short	Assessment of image generation by quantum annealer
title_full	Assessment of image generation by quantum annealer
title_fullStr	Assessment of image generation by quantum annealer
title_full_unstemmed	Assessment of image generation by quantum annealer
title_sort	assessment of image generation by quantum annealer
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/01212a78e03d47b2b0983986af394b84
work_keys_str_mv	AT takehitosato assessmentofimagegenerationbyquantumannealer AT masayukiohzeki assessmentofimagegenerationbyquantumannealer AT kazuyukitanaka assessmentofimagegenerationbyquantumannealer
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Assessment of image generation by quantum annealer

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