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Quantum machine learning

  • Jacob Biamonte
  • , Peter Wittek
  • , Nicola Pancotti
  • , Patrick Rebentrost
  • , Nathan Wiebe
  • , Seth Lloyd
  • Skolkovo Institute of Science and Technology
  • University of Waterloo
  • ICFO The Institute of Photonic Sciences
  • Max Planck Institute of Quantum Optics
  • Massachusetts Institute of Technology
  • Microsoft USA

Résultats de recherche: Contribution à un journalArticle de synthèseRevue par des pairs

3926 Citations (Scopus)

Résumé

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.

langue originaleAnglais
Pages (de - à)195-202
Nombre de pages8
journalNature
Volume549
Numéro de publication7671
Les DOIs
étatPublié - 13 sept. 2017
Modification externeOui

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