Survey on the Improvement and Application of HHL Algorithm

Quantum computing is a new computing mode that follows the laws of quantum mechanics to control quantum information units for computation. In terms of computational efficiency, due to the existence of quantum mechanical superposition, some known quantum algorithms can process problems faster than tr...

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Vydáno v:Journal of physics. Conference series Ročník 2333; číslo 1; s. 12023 - 12034
Hlavní autoři: Liu, Xiaonan, Xie, Haoshan, Liu, Zhengyu, Zhao, Chenyan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Bristol IOP Publishing 01.08.2022
Témata:
Algorithms
HHL Algorithm
Linear equations
LinearSystems
Machine learning
Optimization
Physics
Quantum Computing
Quantum Machine-Learning
Quantum mechanics
Quantum phenomena
Superposition (mathematics)
ISSN:1742-6588, 1742-6596
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Popis
Shrnutí:Quantum computing is a new computing mode that follows the laws of quantum mechanics to control quantum information units for computation. In terms of computational efficiency, due to the existence of quantum mechanical superposition, some known quantum algorithms can process problems faster than traditional general-purpose computers. HHL algorithm is an algorithm for solving linear system problems. Compared with classical algorithms in solving linear equations, it has an exponential acceleration effect in certain cases and as a sub-module, it is widely used in some machine learning algorithms to form quantum machines learning algorithms. However, there are some limiting factors in the use of this algorithm, which affect the overall effect of the algorithm. How to improve it to make the algorithm perform better has become an important issue in the field of quantum computing. This paper summarizes the optimization and improvement of HHL algorithm since it was proposed, and the application of HHL algorithm in machine learning, and discusses some possible future improvements of some subroutines in HHL algorithm.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2333/1/012023