A stochastic quantum program synthesis framework based on Bayesian optimization

Abstract Quantum computers and algorithms can offer exponential performance improvement over some NP-complete programs which cannot be run efficiently through a Von Neumann computing approach. In this paper, we present BayeSyn, which utilizes an enhanced stochastic program synthesis and Bayesian opt...

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Autores principales:	Yao Xiao, Shahin Nazarian, Paul Bogdan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6f99556589954be2bb371e2bcdfb4e5a
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spelling	oai:doaj.org-article:6f99556589954be2bb371e2bcdfb4e5a2021-12-02T18:02:44ZA stochastic quantum program synthesis framework based on Bayesian optimization10.1038/s41598-021-91035-32045-2322https://doaj.org/article/6f99556589954be2bb371e2bcdfb4e5a2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-91035-3https://doaj.org/toc/2045-2322Abstract Quantum computers and algorithms can offer exponential performance improvement over some NP-complete programs which cannot be run efficiently through a Von Neumann computing approach. In this paper, we present BayeSyn, which utilizes an enhanced stochastic program synthesis and Bayesian optimization to automatically generate quantum programs from high-level languages subject to certain constraints. We find that stochastic synthesis can comparatively and efficiently generate a program with a lower cost from the high dimensional program space. We also realize that hyperparameters used in stochastic synthesis play a significant role in determining the optimal program. Therefore, BayeSyn utilizes Bayesian optimization to fine-tune such parameters to generate a suitable quantum program.Yao XiaoShahin NazarianPaul BogdanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yao Xiao Shahin Nazarian Paul Bogdan A stochastic quantum program synthesis framework based on Bayesian optimization
description	Abstract Quantum computers and algorithms can offer exponential performance improvement over some NP-complete programs which cannot be run efficiently through a Von Neumann computing approach. In this paper, we present BayeSyn, which utilizes an enhanced stochastic program synthesis and Bayesian optimization to automatically generate quantum programs from high-level languages subject to certain constraints. We find that stochastic synthesis can comparatively and efficiently generate a program with a lower cost from the high dimensional program space. We also realize that hyperparameters used in stochastic synthesis play a significant role in determining the optimal program. Therefore, BayeSyn utilizes Bayesian optimization to fine-tune such parameters to generate a suitable quantum program.
format	article
author	Yao Xiao Shahin Nazarian Paul Bogdan
author_facet	Yao Xiao Shahin Nazarian Paul Bogdan
author_sort	Yao Xiao
title	A stochastic quantum program synthesis framework based on Bayesian optimization
title_short	A stochastic quantum program synthesis framework based on Bayesian optimization
title_full	A stochastic quantum program synthesis framework based on Bayesian optimization
title_fullStr	A stochastic quantum program synthesis framework based on Bayesian optimization
title_full_unstemmed	A stochastic quantum program synthesis framework based on Bayesian optimization
title_sort	stochastic quantum program synthesis framework based on bayesian optimization
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/6f99556589954be2bb371e2bcdfb4e5a
work_keys_str_mv	AT yaoxiao astochasticquantumprogramsynthesisframeworkbasedonbayesianoptimization AT shahinnazarian astochasticquantumprogramsynthesisframeworkbasedonbayesianoptimization AT paulbogdan astochasticquantumprogramsynthesisframeworkbasedonbayesianoptimization AT yaoxiao stochasticquantumprogramsynthesisframeworkbasedonbayesianoptimization AT shahinnazarian stochasticquantumprogramsynthesisframeworkbasedonbayesianoptimization AT paulbogdan stochasticquantumprogramsynthesisframeworkbasedonbayesianoptimization
_version_	1718378872329732096

A stochastic quantum program synthesis framework based on Bayesian optimization

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