Nature Communications (May 2021)

Power of data in quantum machine learning

  • Hsin-Yuan Huang,
  • Michael Broughton,
  • Masoud Mohseni,
  • Ryan Babbush,
  • Sergio Boixo,
  • Hartmut Neven,
  • Jarrod R. McClean

DOI
https://doi.org/10.1038/s41467-021-22539-9
Journal volume & issue
Vol. 12, no. 1
pp. 1 – 9

Abstract

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Expectations for quantum machine learning are high, but there is currently a lack of rigorous results on which scenarios would actually exhibit a quantum advantage. Here, the authors show how to tell, for a given dataset, whether a quantum model would give any prediction advantage over a classical one.