Artículo
Quantum Machine Learning Implementations: Proposals and Experiments
Autor/es | Lamata Manuel, Lucas |
Departamento | Universidad de Sevilla. Departamento de Física Atómica, Molecular y Nuclear |
Fecha de publicación | 2023 |
Fecha de depósito | 2023-05-03 |
Publicado en |
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Resumen | 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 ... 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 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. |
Agencias financiadoras | Junta de Andalucía Ministerio de Ciencia, Innovación y Universidades (MICINN). España |
Identificador del proyecto | P20-00617
US-1380840 PID2019-104002GB-C21 PID2019-104002GB-C22 |
Cita | Lamata Manuel, L. (2023). Quantum Machine Learning Implementations: Proposals and Experiments. Advanced Quantum Technologies, 2300059. https://doi.org/10.1002/qute.202300059. |
Ficheros | Tamaño | Formato | Ver | Descripción |
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Adv Quantum Tech - 2023 - Lamata ... | 1.095Mb | [PDF] | Ver/ | |