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SoK: Benchmarking the Performance of a Quantum Computer

The quantum computer has been claimed to show more quantum advantage than the classical computer in solving some specific problems. Many companies and research institutes try to develop quantum computers with different physical implementations. Currently, most people only focus on the number of qubi...

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Detalles Bibliográficos
Autores principales: Wang, Junchao, Guo, Guoping, Shan, Zheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9601621/
https://www.ncbi.nlm.nih.gov/pubmed/37420487
http://dx.doi.org/10.3390/e24101467
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author Wang, Junchao
Guo, Guoping
Shan, Zheng
author_facet Wang, Junchao
Guo, Guoping
Shan, Zheng
author_sort Wang, Junchao
collection PubMed
description The quantum computer has been claimed to show more quantum advantage than the classical computer in solving some specific problems. Many companies and research institutes try to develop quantum computers with different physical implementations. Currently, most people only focus on the number of qubits in a quantum computer and consider it as a standard to evaluate the performance of the quantum computer intuitively. However, it is quite misleading in most times, especially for investors or governments. This is because the quantum computer works in a quite different way than classical computers. Thus, quantum benchmarking is of great importance. Currently, many quantum benchmarks are proposed from different aspects. In this paper, we review the existing performance benchmarking protocols, models, and metrics. We classify the benchmarking techniques into three categories: physical benchmarking, aggregative benchmarking, and application-level benchmarking. We also discuss the future trend for quantum computer’s benchmarking and propose setting up the QTOP100.
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spelling pubmed-96016212022-10-27 SoK: Benchmarking the Performance of a Quantum Computer Wang, Junchao Guo, Guoping Shan, Zheng Entropy (Basel) Review The quantum computer has been claimed to show more quantum advantage than the classical computer in solving some specific problems. Many companies and research institutes try to develop quantum computers with different physical implementations. Currently, most people only focus on the number of qubits in a quantum computer and consider it as a standard to evaluate the performance of the quantum computer intuitively. However, it is quite misleading in most times, especially for investors or governments. This is because the quantum computer works in a quite different way than classical computers. Thus, quantum benchmarking is of great importance. Currently, many quantum benchmarks are proposed from different aspects. In this paper, we review the existing performance benchmarking protocols, models, and metrics. We classify the benchmarking techniques into three categories: physical benchmarking, aggregative benchmarking, and application-level benchmarking. We also discuss the future trend for quantum computer’s benchmarking and propose setting up the QTOP100. MDPI 2022-10-14 /pmc/articles/PMC9601621/ /pubmed/37420487 http://dx.doi.org/10.3390/e24101467 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Wang, Junchao
Guo, Guoping
Shan, Zheng
SoK: Benchmarking the Performance of a Quantum Computer
title SoK: Benchmarking the Performance of a Quantum Computer
title_full SoK: Benchmarking the Performance of a Quantum Computer
title_fullStr SoK: Benchmarking the Performance of a Quantum Computer
title_full_unstemmed SoK: Benchmarking the Performance of a Quantum Computer
title_short SoK: Benchmarking the Performance of a Quantum Computer
title_sort sok: benchmarking the performance of a quantum computer
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9601621/
https://www.ncbi.nlm.nih.gov/pubmed/37420487
http://dx.doi.org/10.3390/e24101467
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