Automated optimization of large quantum circuits with continuous parameters

Quantum computation: optimizing quantum circuits A new software tool significantly reduces the size of arbitrary quantum circuits, automatically optimizing the number of gates required for running algorithms. Yunseong Nam and colleagues from the University of Maryland developed a set of subroutines...

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Autores principales:	Yunseong Nam, Neil J. Ross, Yuan Su, Andrew M. Childs, Dmitri Maslov
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Physics QC1-999 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/db98af5e361345b4b60bf97577dc99cb
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spelling	oai:doaj.org-article:db98af5e361345b4b60bf97577dc99cb2021-12-02T11:42:16ZAutomated optimization of large quantum circuits with continuous parameters10.1038/s41534-018-0072-42056-6387https://doaj.org/article/db98af5e361345b4b60bf97577dc99cb2018-05-01T00:00:00Zhttps://doi.org/10.1038/s41534-018-0072-4https://doaj.org/toc/2056-6387Quantum computation: optimizing quantum circuits A new software tool significantly reduces the size of arbitrary quantum circuits, automatically optimizing the number of gates required for running algorithms. Yunseong Nam and colleagues from the University of Maryland developed a set of subroutines which, given a certain quantum circuit, would remove redundant gates by changing the order of individual or multiple operations and combining them. After a pre-processing phase, the execution of these routines in careful order constitutes a powerful automatized approach for reducing the resources required to implement a given algorithm. The heuristic nature of this optimization makes its computational cost scale well with the size of the circuit, as shown by comparisons for the computation of discrete logarithms and Hamiltonian simulations. This makes it applicable to computations that can be run on existing hardware and might outperform classical computers.Yunseong NamNeil J. RossYuan SuAndrew M. ChildsDmitri MaslovNature PortfolioarticlePhysicsQC1-999Electronic computers. Computer scienceQA75.5-76.95ENnpj Quantum Information, Vol 4, Iss 1, Pp 1-12 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Physics QC1-999 Electronic computers. Computer science QA75.5-76.95
spellingShingle	Physics QC1-999 Electronic computers. Computer science QA75.5-76.95 Yunseong Nam Neil J. Ross Yuan Su Andrew M. Childs Dmitri Maslov Automated optimization of large quantum circuits with continuous parameters
description	Quantum computation: optimizing quantum circuits A new software tool significantly reduces the size of arbitrary quantum circuits, automatically optimizing the number of gates required for running algorithms. Yunseong Nam and colleagues from the University of Maryland developed a set of subroutines which, given a certain quantum circuit, would remove redundant gates by changing the order of individual or multiple operations and combining them. After a pre-processing phase, the execution of these routines in careful order constitutes a powerful automatized approach for reducing the resources required to implement a given algorithm. The heuristic nature of this optimization makes its computational cost scale well with the size of the circuit, as shown by comparisons for the computation of discrete logarithms and Hamiltonian simulations. This makes it applicable to computations that can be run on existing hardware and might outperform classical computers.
format	article
author	Yunseong Nam Neil J. Ross Yuan Su Andrew M. Childs Dmitri Maslov
author_facet	Yunseong Nam Neil J. Ross Yuan Su Andrew M. Childs Dmitri Maslov
author_sort	Yunseong Nam
title	Automated optimization of large quantum circuits with continuous parameters
title_short	Automated optimization of large quantum circuits with continuous parameters
title_full	Automated optimization of large quantum circuits with continuous parameters
title_fullStr	Automated optimization of large quantum circuits with continuous parameters
title_full_unstemmed	Automated optimization of large quantum circuits with continuous parameters
title_sort	automated optimization of large quantum circuits with continuous parameters
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/db98af5e361345b4b60bf97577dc99cb
work_keys_str_mv	AT yunseongnam automatedoptimizationoflargequantumcircuitswithcontinuousparameters AT neiljross automatedoptimizationoflargequantumcircuitswithcontinuousparameters AT yuansu automatedoptimizationoflargequantumcircuitswithcontinuousparameters AT andrewmchilds automatedoptimizationoflargequantumcircuitswithcontinuousparameters AT dmitrimaslov automatedoptimizationoflargequantumcircuitswithcontinuousparameters
_version_	1718395372223594496

Automated optimization of large quantum circuits with continuous parameters

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