A survey on HHL algorithm: From theory to application in quantum machine learning

•Reviewing the necessary background on elementary quantum algorithms.•Explain how HHL is used in quantum machine learning models, and how it enables quantum speedup.•Briefly discuss the remaining challenges ahead for HHL-based QML models and related methods. The Harrow-Hassidim-Lloyd (HHL) algorithm...

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Bibliographic Details
Published inPhysics letters. A Vol. 384; no. 24; p. 126595
Main Authors Duan, Bojia, Yuan, Jiabin, Yu, Chao-Hua, Huang, Jianbang, Hsieh, Chang-Yu
Format Journal Article
LanguageEnglish
Published Elsevier B.V 28.08.2020
Subjects
HHL algorithm
Quantum circuit
Quantum computation
Quantum machine learning
Quantum computation
Quantum machine learning
Quantum circuit
HHL algorithm
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Summary:•Reviewing the necessary background on elementary quantum algorithms.•Explain how HHL is used in quantum machine learning models, and how it enables quantum speedup.•Briefly discuss the remaining challenges ahead for HHL-based QML models and related methods. The Harrow-Hassidim-Lloyd (HHL) algorithm is a method to solve the quantum linear system of equations that may be found at the core of various scientific applications and quantum machine learning models including the linear regression, support vector machines and recommender systems etc. After reviewing the necessary background on elementary quantum algorithms, we provide detailed account of how HHL is exploited in different quantum machine learning (QML) models, and how it provides the desired quantum speedup in all these models. At the end, we briefly discuss some of the remaining challenges ahead for HHL-based QML models and related methods.
ISSN:0375-9601
1873-2429
DOI:10.1016/j.physleta.2020.126595