Quantum machine learning

Jacob Biamonte (), Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe and Seth Lloyd
Additional contact information
Jacob Biamonte: Quantum Complexity Science Initiative, Skolkovo Institute of Science and Technology
Peter Wittek: ICFO—The Institute of Photonic Sciences
Nicola Pancotti: Max Planck Institute of Quantum Optics
Patrick Rebentrost: Massachusetts Institute of Technology, Research Laboratory of Electronics
Nathan Wiebe: Station Q Quantum Architectures and Computation Group, Microsoft Research
Seth Lloyd: Massachusetts Institute of Technology

Nature, 2017, vol. 549, issue 7671, 195-202

Abstract: Abstract Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Quantum systems produce atypical patterns that classical systems are thought not to produce efficiently, so it is reasonable to postulate that quantum computers may outperform classical computers on machine learning tasks. The field of quantum machine learning explores how to devise and implement quantum software that could enable machine learning that is faster than that of classical computers. Recent work has produced quantum algorithms that could act as the building blocks of machine learning programs, but the hardware and software challenges are still considerable.

Date: 2017
References: Add references at CitEc
Citations: View citations in EconPapers (49)

Downloads: (external link)
https://www.nature.com/articles/nature23474 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:nature:v:549:y:2017:i:7671:d:10.1038_nature23474

Ordering information: This journal article can be ordered from
https://www.nature.com/

DOI: 10.1038/nature23474

Access Statistics for this article

Nature is currently edited by Magdalena Skipper

More articles in Nature from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().