The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Quantum-Enhanced Machine Learning
Open accessAbstract
The emerging field of quantum machine learning has the potential to substantially aid in the problems and scope of artificial intelligence. This is only enhanced by recent successes in the field of classical machine learning. In this work we propose an approach for the systematic treatment of machine learning, from the perspective of quantum information. Our approach is general and covers all three main branches of machine learning: supervised, unsupervised, and reinforcement learning. While quantum improvements in supervised and unsupervised learning have been reported, reinforcement learning has received much less attention. Within our approach, we tackle the problem of quantum enhancements in reinforcement learning as well, and propose a systematic scheme for providing improvements. As an example, we show that quadratic improvements in learning efficiency, and exponential improvements in performance over limited time periods, can be obtained for a broad class of learning problems.
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Citations
Cites
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Cited by
Sorted by citations
Machine learning for molecular and materials science
Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018) · Nature · doi:10.1038/s41586-018-0337-2 · Cited by 4,968
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Noisy intermediate-scale quantum algorithms
Bharti, K., Cervera-Lierta, A., Kyaw, T. H., Haug, T., Alperin-Lea, S., Anand, A., Degroote, M., Heimonen, H., Kottmann, J. S., Menke, T., et al. (2022) · Reviews of Modern Physics · doi:10.1103/revmodphys.94.015004 · Cited by 2,231
Quantum convolutional neural networks
Cong, I., Choi, S., & Lukin, M. D. (2019) · Nature Physics · doi:10.1038/s41567-019-0648-8 · Cited by 1,517
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Self-citation
Metadata
- Authors
- Dunjko, V., Taylor, J. M., & Briegel, H. J.
- Affiliations
- Universität Innsbruck (AT)
- National Institute of Standards and Technology (US)
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2016-09-20
- Volume
- 117
- Issue
- 13
- Pages
- 130501-130501
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W2521267242
- Cited by
- 460
- References
- 23
- Citations by year
- 2026: 37 · 2025: 69 · 2024: 60 · 2023: 54 · 2022: 50 · 2021: 63 · 2020: 56 · 2019: 31 · 2018: 23 · 2017: 17
- FWCI
- 19.50
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Physical Review Letters (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Austrian Science Fund (SFB FoQuS F 4012)
- Templeton World Charity Foundation (TWCF0078/AB46)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum Information and Cryptography
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
- Keywords
- quantum machine learning, supervised learning, unsupervised learning, quantum information, learning efficiency, quantum advantage
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