A comprehensive review of Quantum Machine Learning: from NISQ to Fault Tolerance

Yunfei Wang, Junyu Liu
1/21/2024
quant-phcs.AIcs.LG

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Abstract

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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Cite this paper

@article{wang2024comprehensive,
  title  = {A comprehensive review of Quantum Machine Learning: from NISQ to Fault Tolerance},
  author = {Yunfei Wang and Junyu Liu},
  year   = {2024},
  eprint = {2401.11351},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2401.11351v2}
}

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