arXiv:2303.09491 [quant-ph]AbstractReferencesReviewsResources
Challenges and Opportunities in Quantum Machine Learning
M. Cerezo, Guillaume Verdon, Hsin-Yuan Huang, Lukasz Cincio, Patrick J. Coles
Published 2023-03-16Version 1
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.
Comments: 14 pages, 5 figures
Journal: Nature Computational Science 2, 567-576 (2022)
Keywords: quantum machine learning, opportunities, review current methods, quantum neural networks, quantum data
Tags: journal article
Related articles: Most relevant | Search more
arXiv:2311.12618 [quant-ph] (Published 2023-11-21)
Limitations of measure-first protocols in quantum machine learning
arXiv:2310.10315 [quant-ph] (Published 2023-10-16)
A Survey on Quantum Machine Learning: Current Trends, Challenges, Opportunities, and the Road Ahead
arXiv:1707.08561 [quant-ph] (Published 2017-07-26)
Quantum machine learning: a classical perspective