  This article gives an overview and a perspective of recent theoretical
proposals and their experimental implementations in the field of quantum
machine learning. Without an aim to being exhaustive, the article reviews
specific high-impact topics such as quantum reinforcement learning, quantum
autoencoders, and quantum memristors, and their experimental realizations in
the platforms of quantum photonics and superconducting circuits. The field of
quantum machine learning could be among the first quantum technologies
producing results that are beneficial for industry and, in turn, to society.
Therefore, it is necessary to push forward initial quantum implementations of
this technology, in Noisy Intermediate-Scale Quantum Computers, aiming for
achieving fruitful calculations in machine learning that are better than with
any other current or future computing paradigm.
