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Quantum machine learning

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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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Figure 1: Quantum tunnelling versus thermalization.

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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.

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Authors and 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, 08860, Barcelona, Spain

    Peter Wittek

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

    Nicola Pancotti

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

    Patrick Rebentrost

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

    Nathan Wiebe

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

    Seth Lloyd

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

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Biamonte, J., Wittek, P., Pancotti, N. et al. Quantum machine learning. Nature 549, 195–202 (2017). https://doi.org/10.1038/nature23474

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