Quantum Machine Learning Implementations: Proposals and Experiments

• URL: http://arxiv.org/abs/2303.06263v1
• Date: Sat, 11 Mar 2023 01:02:16 GMT
• Title: Quantum Machine Learning Implementations: Proposals and Experiments
• Authors: Lucas Lamata
• Abstract summary: The article reviews specific high-impact topics such as quantum reinforcement learning, quantum autoencoders, and quantum memristors. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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