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Quantum machine learning

Author

Listed:
  • Jacob Biamonte

    (Quantum Complexity Science Initiative, Skolkovo Institute of Science and Technology
    Institute for Quantum Computing, University of Waterloo)

  • 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)

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.

Suggested Citation

  • Jacob Biamonte & Peter Wittek & Nicola Pancotti & Patrick Rebentrost & Nathan Wiebe & Seth Lloyd, 2017. "Quantum machine learning," Nature, Nature, vol. 549(7671), pages 195-202, September.
    • Handle: RePEc:nat:nature:v:549:y:2017:i:7671:d:10.1038_nature23474
    DOI: 10.1038/nature23474
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