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arXiv:1611.09347quant-ph

Quantum Machine Learning

Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, Seth Lloyd

Early discovery. Linked by one identifiable commenter. This paper is outside the main ranking; a mention is not an endorsement.

Abstract

Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Since quantum systems produce counter-intuitive patterns believed not to be efficiently produced by classical systems, 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 concrete quantum software that offers such advantages. Recent work has made clear that the hardware and software challenges are still considerable but has also opened paths towards solutions.

arXiv comments
24 pages, 2 figures
Journal reference
Nature 549, 195-202 (2017)
DOI
doi:10.1038/nature23474
Subjects
quant-phQuantum Physics cond-mat.str-elStrongly Correlated Electrons stat.MLMachine Learning (Statistics)

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Linked by 1 identifiable commenter in 1 comment across 1 thread, from Oct 2017 to Oct 2017. Found in 2 HN submissions from Nov 2016 to Oct 2017. Open submission discussions.

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Discussed in comments1 thread

    • There's a lot of work on this, although no breakthrough applications yet. ------ "Quantum Machine Learning" Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, Seth Lloyd Recent progress implies that a crossover between machine learning and quantum…

      jessriedelView on HNParent context

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