Quantum Machine Learning Implementations: Proposals and Experiments

Author:

Lamata Lucas12ORCID

Affiliation:

1. Departamento de Física Atómica, Molecular, y Nuclear, Facultad de Física Universidad de Sevilla Apartado 1065 Sevilla 41080 Spain

2. Instituto Carlos I de Física Teórica y Computacional Universidad de Granada Granada 18071 Spain

Abstract

AbstractThis 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 can 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.

Funder

Consejería de Economía, Innovación, Ciencia y Empleo, Junta de Andalucía

Ministerio de Ciencia e Innovación

Publisher

Wiley

Subject

Electrical and Electronic Engineering,Computational Theory and Mathematics,Condensed Matter Physics,Mathematical Physics,Nuclear and High Energy Physics,Electronic, Optical and Magnetic Materials,Statistical and Nonlinear Physics

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