The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Experimental Realization of a Quantum Support Vector Machine
Open accessAbstract
The fundamental principle of artificial intelligence is the ability of machines to learn from previous experience and do future work accordingly. In the age of big data, classical learning machines often require huge computational resources in many practical cases. Quantum machine learning algorithms, on the other hand, could be exponentially faster than their classical counterparts by utilizing quantum parallelism. Here, we demonstrate a quantum machine learning algorithm to implement handwriting recognition on a four-qubit NMR test bench. The quantum machine learns standard character fonts and then recognizes handwritten characters from a set with two candidates. Because of the wide spread importance of artificial intelligence and its tremendous consumption of computational resources, quantum speedup would be extremely attractive against the challenges of big data.
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Cites
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Cited by
Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., & Lloyd, S. (2017) · Nature · doi:10.1038/nature23474 · Cited by 4,779
Quantum computing for finance: Overview and prospects
Orús, R., Mugel, S., & Lizaso, E. (2019) · Reviews in Physics · doi:10.1016/j.revip.2019.100028 · Cited by 668
A Survey on quantum computing technology
Gyongyosi, L., & Imre, S. (2019) · Computer Science Review · doi:10.1016/j.cosrev.2018.11.002 · Cited by 646
Implementing a distance-based classifier with a quantum interference circuit
Schuld, M., Fingerhuth, M., & Petruccione, F. (2017) · EPL (Europhysics Letters) · doi:10.1209/0295-5075/119/60002 · Cited by 285
Distributed secure quantum machine learning
Sheng, Y.-B., & Zhou, L. (2017) · Science Bulletin · doi:10.1016/j.scib.2017.06.007 · Cited by 245
Metadata
- Authors
- Li, Z., Liu, X., Xu, N., & Du, J.
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2015-04-08
- Volume
- 114
- Issue
- 14
- Pages
- 140504-140504
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W1529944915
- Cited by
- 259
- References
- 30
- FWCI
- 8.13
- Type
- journal article
- Language
- en
- Open access
- green
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
