A comprehensive review of quantum machine learning: from NISQ to fault tolerance

Quantum machine learning, which involves running machine learning algorithms on quantum devices, has garnered significant attention in both academic and business circles. In this paper, we offer a comprehensive and unbiased review of the various concepts that have emerged in the field of quantum mac...

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Vydané v:Reports on progress in physics Ročník 87; číslo 11
Hlavní autori: Wang, Yunfei, Liu, Junyu
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: England 01.11.2024
Predmet:
machine learning
quantum algorithms
quantum computing
ISSN:1361-6633, 1361-6633
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Shrnutí:Quantum machine learning, which involves running machine learning algorithms on quantum devices, has garnered significant attention in both academic and business circles. In this paper, we offer a comprehensive and unbiased review of the various concepts that have emerged in the field of quantum machine learning. This includes techniques used in Noisy Intermediate-Scale Quantum (NISQ) technologies and approaches for algorithms compatible with fault-tolerant quantum computing hardware. Our review covers fundamental concepts, algorithms, and the statistical learning theory pertinent to quantum machine learning.
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ISSN:1361-6633
1361-6633
DOI:10.1088/1361-6633/ad7f69