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Hybrid classical-quantum linear solver using Noisy Intermediate-Scale Quantum machines

We propose a realistic hybrid classical-quantum linear solver to solve systems of linear equations of a specific type, and demonstrate its feasibility with Qiskit on IBM Q systems. This algorithm makes use of quantum random walk that runs in [Formula: see text] (N log(N)) time on a quantum circuit m...

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Autores principales: Chen, Chih-Chieh, Shiau, Shiue-Yuan, Wu, Ming-Feng, Wu, Yuh-Renn
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6838121/
https://www.ncbi.nlm.nih.gov/pubmed/31700001
http://dx.doi.org/10.1038/s41598-019-52275-6
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author Chen, Chih-Chieh
Shiau, Shiue-Yuan
Wu, Ming-Feng
Wu, Yuh-Renn
author_facet Chen, Chih-Chieh
Shiau, Shiue-Yuan
Wu, Ming-Feng
Wu, Yuh-Renn
author_sort Chen, Chih-Chieh
collection PubMed
description We propose a realistic hybrid classical-quantum linear solver to solve systems of linear equations of a specific type, and demonstrate its feasibility with Qiskit on IBM Q systems. This algorithm makes use of quantum random walk that runs in [Formula: see text] (N log(N)) time on a quantum circuit made of [Formula: see text] (log(N)) qubits. The input and output are classical data, and so can be easily accessed. It is robust against noise, and ready for implementation in applications such as machine learning.
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spelling pubmed-68381212019-11-14 Hybrid classical-quantum linear solver using Noisy Intermediate-Scale Quantum machines Chen, Chih-Chieh Shiau, Shiue-Yuan Wu, Ming-Feng Wu, Yuh-Renn Sci Rep Article We propose a realistic hybrid classical-quantum linear solver to solve systems of linear equations of a specific type, and demonstrate its feasibility with Qiskit on IBM Q systems. This algorithm makes use of quantum random walk that runs in [Formula: see text] (N log(N)) time on a quantum circuit made of [Formula: see text] (log(N)) qubits. The input and output are classical data, and so can be easily accessed. It is robust against noise, and ready for implementation in applications such as machine learning. Nature Publishing Group UK 2019-11-07 /pmc/articles/PMC6838121/ /pubmed/31700001 http://dx.doi.org/10.1038/s41598-019-52275-6 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Chen, Chih-Chieh
Shiau, Shiue-Yuan
Wu, Ming-Feng
Wu, Yuh-Renn
Hybrid classical-quantum linear solver using Noisy Intermediate-Scale Quantum machines
title Hybrid classical-quantum linear solver using Noisy Intermediate-Scale Quantum machines
title_full Hybrid classical-quantum linear solver using Noisy Intermediate-Scale Quantum machines
title_fullStr Hybrid classical-quantum linear solver using Noisy Intermediate-Scale Quantum machines
title_full_unstemmed Hybrid classical-quantum linear solver using Noisy Intermediate-Scale Quantum machines
title_short Hybrid classical-quantum linear solver using Noisy Intermediate-Scale Quantum machines
title_sort hybrid classical-quantum linear solver using noisy intermediate-scale quantum machines
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6838121/
https://www.ncbi.nlm.nih.gov/pubmed/31700001
http://dx.doi.org/10.1038/s41598-019-52275-6
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