Challenges and Opportunities in Quantum Machine Learning
Quantum Physics
2023-03-17 v1 Machine Learning
Machine Learning
Abstract
At the intersection of machine learning and quantum computing, Quantum Machine Learning (QML) has the potential of accelerating data analysis, especially for quantum data, with applications for quantum materials, biochemistry, and high-energy physics. Nevertheless, challenges remain regarding the trainability of QML models. Here we review current methods and applications for QML. We highlight differences between quantum and classical machine learning, with a focus on quantum neural networks and quantum deep learning. Finally, we discuss opportunities for quantum advantage with QML.
Cite
@article{arxiv.2303.09491,
title = {Challenges and Opportunities in Quantum Machine Learning},
author = {M. Cerezo and Guillaume Verdon and Hsin-Yuan Huang and Lukasz Cincio and Patrick J. Coles},
journal= {arXiv preprint arXiv:2303.09491},
year = {2023}
}
Comments
14 pages, 5 figures