  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.
