Quantum Machine Learning Implementations: Proposals and Experiments
This is an incremental review aimed at researchers in quantum computing and machine learning, emphasizing the need to advance initial implementations in noisy intermediate-scale quantum computers.
The article reviews recent theoretical proposals and experimental implementations in quantum machine learning, covering topics like quantum reinforcement learning and quantum autoencoders, and highlights the potential for quantum technologies to produce industry-beneficial results.
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.