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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
Quantum computation and quantum information
Law, J. (2001) · ACM SIGSOFT Software Engineering Notes · doi:10.1145/505482.505499 · Cited by 20,450
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
Artificial intelligence: A modern approach
Nilsson, N. J. (1996) · Artificial Intelligence · doi:10.1016/0004-3702(96)00007-0 · Cited by 10,730
Quantum Support Vector Machine for Big Data Classification
Rebentrost, P., Mohseni, M., & Lloyd, S. (2014) · Physical Review Letters · doi:10.1103/physrevlett.113.130503 · Cited by 1,951
Quantum Measurement and Control
Kiefer, C. (2010) · Classical and Quantum Gravity · doi:10.1088/0264-9381/27/24/249002 · Cited by 1,016
Giovannetti, V., Lloyd, S., & Maccone, L. (2008) · Physical Review Letters · doi:10.1103/physrevlett.100.160501 · Cited by 935
(1996) · Cited by 824
The quest for a Quantum Neural Network
Schuld, M., Sinayskiy, I., & Petruccione, F. (2014) · Quantum Information Processing · doi:10.1007/s11128-014-0809-8 · Cited by 671
Quantum Reinforcement Learning
Dong, D., Chen, C., Li, H., & Tarn, T.-J. (2008) · IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) · doi:10.1109/tsmcb.2008.925743 · Cited by 410
Quantum Machine Learning: What Quantum Computing Means to Data Mining
Wittek, P. (2014) · Cited by 319
Fixed-Point Quantum Search with an Optimal Number of Queries
Yoder, T. J., Low, G. H., & Chuang, I. L. (2014) · Physical Review Letters · doi:10.1103/physrevlett.113.210501 · Cited by 267
Automated Search for new Quantum Experiments
Krenn, M., Malik, M., Fickler, R., Łapkiewicz, R., & Zeilinger, A. (2016) · Physical Review Letters · doi:10.1103/physrevlett.116.090405 · Cited by 246
Quantum speed-up for unsupervised learning
Aı̈meur, E., Brassard, G., & Gambs, S. (2012) · Machine Learning · doi:10.1007/s10994-012-5316-5 · Cited by 218
Quantum Speedup for Active Learning Agents
Paparo, G. D., Dunjko, V., Makmal, A., Martin-Delgado, M. A., & Briegel, H. J. (2014) · Physical Review X · doi:10.1103/physrevx.4.031002 · Cited by 192
Self-citationProjective simulation for artificial intelligence
Briegel, H. J., & Cuevas, G. D. L. (2012) · Scientific Reports · doi:10.1038/srep00400 · Cited by 158
Self-citationGrover's Quantum Algorithm Applied to Global Optimization
Baritompa, W. P., Bulger, D. W., & Wood, G. R. (2005) · SIAM Journal on Optimization · doi:10.1137/040605072 · Cited by 108
Evolutionary algorithms for hard quantum control
Zahedinejad, E., Schirmer, S. G., & Sanders, B. C. (2014) · Physical Review A · doi:10.1103/physreva.90.032310 · Cited by 85
Differential Evolution for Many-Particle Adaptive Quantum Metrology
Lovett, N. B., de Bellaistre, C. C., Perarnau-Llobet, M., & Sanders, B. C. (2013) · Physical Review Letters · doi:10.1103/physrevlett.110.220501 · Cited by 74
Adaptive quantum computation in changing environments using projective simulation
Tiersch, M., Ganahl, E. J., & Briegel, H. J. (2015) · Scientific Reports · doi:10.1038/srep12874 · Cited by 56
Self-citationMeta-learning within Projective Simulation
Makmal, A., Melnikov, A. A., Dunjko, V., & Briegel, H. J. (2016) · IEEE Access · doi:10.1109/access.2016.2556579 · Cited by 36
Self-citationChouard, T., & Venema, L. C. (2015) · Nature · doi:10.1038/521435a · Cited by 31
Stajic, J., Stone, R., Chin, G., & Wible, B. (2015) · Science · doi:10.1126/science.349.6245.248 · Cited by 30
Research works printed but not linked: 5
- R. A. Servedio, 2001, 16th Annual IEEE Conference on Computational Complexity, Chicago, Illinois, 2001
- E. Aïmeur, 2006, Advances in Artificial Intelligence: 19th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2006
- L. K. Grover, 1996, Proceedings, 28th Annual ACM Symposium on the Theory of Computing, Philadelphia, 1996
- R. S. Sutton, 1990, Proceedings of the Seventh International Workshop on Machine Learning, Austin TX, 1990
- 10.1002/(SICI)1521-3978(199806)46:4/5<493::AID-PROP493>3.0.CO;2-P · DOI
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
Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., & Lloyd, S. (2017) · Nature · doi:10.1038/nature23474 · Cited by 4,779
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
Machine learning & artificial intelligence in the quantum domain: a review of recent progress
Dunjko, V., & Briegel, H. J. (2018) · Reports on Progress in Physics · doi:10.1088/1361-6633/aab406 · Cited by 1,241
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
