The DOI matches its Crossref record; 131 of the 131 references it cites were found in a registry or an index.
Challenges and opportunities in quantum machine learning
Open accessAbstract
At the intersection of machine learning and quantum computing, quantum machine learning 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 quantum machine learning models. Here we review current methods and applications for quantum machine learning. 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 quantum machine learning. Quantum machine learning has become an essential tool to process and analyze the increased amount of quantum data. Despite recent progress, there are still many challenges to be addressed and myriad future avenues of research.
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Cites
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Cited by
Quantum Computing for Molecular Biology**
Baiardi, A., Christandl, M., & Reiher, M. (2023) · ChemBioChem · doi:10.1002/cbic.202300120 · Cited by 93
Quantum convolutional neural networks for multi-channel supervised learning
(2023) · doi:10.1007/s42484-023-00130-3 · Cited by 32
Introduction to quantum error correction with stabilizer codes
Bradshaw, Z. P., Dale, J. J., & Evans, E. N. (2026) · Annals of Physics · doi:10.1016/j.aop.2026.170353 · Cited by 4
Li, S., Salek, M. S., Chowdhury, M., & Wang, Y. (2026) · Physical Review Research · doi:10.1103/wb1b-ntpc
Metadata
- Authors
- Cerezo, M., Verdon, G., Huang, H.-Y., Cincio, L., & Coles, P. J.
- Venue
- Nature Computational Science
- Published
- 2022-09-15
- Volume
- 2
- Issue
- 9
- Pages
- 567-576
- ISSN
- 2662-8457 (ISSN-L)
- OpenAlex
- W4296126438
- Cited by
- 689
- References
- 131
- FWCI
- 40.47
- Type
- journal article
- Language
- en
- Open access
- green
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