Review Article

Quantum machine learning

  • Nature volume 549, pages 195202 (14 September 2017)
  • doi:10.1038/nature23474
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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.

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Acknowledgements

J.B. acknowledges financial support from AFOSR grant FA9550-16-1-0300, Models and Protocols for Quantum Distributed Computation. P.W. acknowledges financial support from the ERC (Consolidator Grant QITBOX), Spanish Ministry of Economy and Competitiveness (Severo Ochoa Programme for Centres of Excellence in R&D SEV-2015-0522 and QIBEQI FIS2016-80773-P), Generalitat de Catalunya (CERCA Programme and SGR 875), and Fundacio Privada Cellex. P.R. and S.L. acknowledge funding from ARO and AFOSR under MURI programmes. We thank L. Zheglova for producing Fig. 1.

Author information

Affiliations

  1. Quantum Complexity Science Initiative, Skolkovo Institute of Science and Technology, Skoltech Building 3, Moscow 143026, Russia

    • Jacob Biamonte
  2. Institute for Quantum Computing, University of Waterloo, Waterloo, N2L 3G1 Ontario, Canada

    • Jacob Biamonte
  3. ICFO—The Institute of Photonic Sciences, Castelldefels, Barcelona 08860 Spain

    • Peter Wittek
  4. Max Planck Institute of Quantum Optics, 1 Hans-Kopfermannstrasse, D-85748 Garching, Germany

    • Nicola Pancotti
  5. Massachusetts Institute of Technology, Research Laboratory of Electronics, Cambridge, Massachusetts 02139, USA

    • Patrick Rebentrost
  6. Station Q Quantum Architectures and Computation Group, Microsoft Research, Redmond, Washington 98052, USA

    • Nathan Wiebe
  7. Massachusetts Institute of Technology, Department of Mechanical Engineering, Cambridge, Massachusetts 02139, USA

    • Seth Lloyd

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Contributions

All authors designed the study, analysed data, interpreted data, produced Box 3 Figure and wrote the article.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Jacob Biamonte.

Reviewer Information Nature thanks L. Lamata and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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