Quantum machine learning

doi:10.1038/nature23474

. 2017 Sep 13;549(7671):195-202.

doi: 10.1038/nature23474.

Quantum machine learning

Jacob Biamonte^{1

2}, Peter Wittek³, Nicola Pancotti⁴, Patrick Rebentrost⁵, Nathan Wiebe⁶, Seth Lloyd⁷

Affiliations

¹ Quantum Complexity Science Initiative, Skolkovo Institute of Science and Technology, Skoltech Building 3, Moscow 143026, Russia.
² Institute for Quantum Computing, University of Waterloo, Waterloo, N2L 3G1 Ontario, Canada.
³ ICFO-The Institute of Photonic Sciences, Castelldefels, Barcelona 08860 Spain.
⁴ Max Planck Institute of Quantum Optics, 1 Hans-Kopfermannstrasse, D-85748 Garching, Germany.
⁵ Massachusetts Institute of Technology, Research Laboratory of Electronics, Cambridge, Massachusetts 02139, USA.
⁶ Station Q Quantum Architectures and Computation Group, Microsoft Research, Redmond, Washington 98052, USA.
⁷ Massachusetts Institute of Technology, Department of Mechanical Engineering, Cambridge, Massachusetts 02139, USA.

PMID: 28905917
DOI: 10.1038/nature23474

Quantum machine learning

Jacob Biamonte et al. Nature. 2017.

. 2017 Sep 13;549(7671):195-202.

doi: 10.1038/nature23474.

Authors

Jacob Biamonte^{1

2}, Peter Wittek³, Nicola Pancotti⁴, Patrick Rebentrost⁵, Nathan Wiebe⁶, Seth Lloyd⁷

Affiliations

¹ Quantum Complexity Science Initiative, Skolkovo Institute of Science and Technology, Skoltech Building 3, Moscow 143026, Russia.
² Institute for Quantum Computing, University of Waterloo, Waterloo, N2L 3G1 Ontario, Canada.
³ ICFO-The Institute of Photonic Sciences, Castelldefels, Barcelona 08860 Spain.
⁴ Max Planck Institute of Quantum Optics, 1 Hans-Kopfermannstrasse, D-85748 Garching, Germany.
⁵ Massachusetts Institute of Technology, Research Laboratory of Electronics, Cambridge, Massachusetts 02139, USA.
⁶ Station Q Quantum Architectures and Computation Group, Microsoft Research, Redmond, Washington 98052, USA.
⁷ Massachusetts Institute of Technology, Department of Mechanical Engineering, Cambridge, Massachusetts 02139, USA.

PMID: 28905917
DOI: 10.1038/nature23474

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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References

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[1] Sci Rep. 2017 Apr 19;7:45672 - PubMed

[2] Sci Rep. 2017 Apr 19;7:45672 - PubMed

[3] Phys Rev Lett. 2013 May 31;110(22):220501 - PubMed

[4] Phys Rev Lett. 2013 May 31;110(22):220501 - PubMed

[5] Phys Rev Lett. 2012 Aug 3;109(5):050505 - PubMed

[6] Phys Rev Lett. 2012 Aug 3;109(5):050505 - PubMed

[7] Phys Rev Lett. 2013 Jun 21;110(25):250504 - PubMed

[8] Phys Rev Lett. 2013 Jun 21;110(25):250504 - PubMed

[9] Science. 2014 Jul 25;345(6195):420-4 - PubMed

[10] Science. 2014 Jul 25;345(6195):420-4 - PubMed

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Quantum machine learning

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Quantum machine learning

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