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arXiv:1307.0411quant-ph

Quantum algorithms for supervised and unsupervised machine learning

Seth Lloyd, Masoud Mohseni, Patrick Rebentrost

Abstract

Machine-learning tasks frequently involve problems of manipulating and classifying large numbers of vectors in high-dimensional spaces. Classical algorithms for solving such problems typically take time polynomial in the number of vectors and the dimension of the space. Quantum computers are good at manipulating high-dimensional vectors in large tensor product spaces. This paper provides supervised and unsupervised quantum machine learning algorithms for cluster assignment and cluster finding. Quantum machine learning can take time logarithmic in both the number of vectors and their dimension, an exponential speed-up over classical algorithms.

arXiv comments
11 pages, Plain TeX
Journal reference
DOI
Subjects
quant-phQuantum Physics

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Linked by 2 identifiable commenters in 2 comments across 2 threads, from Oct 2013 to Oct 2014. Found in 1 HN submission from Oct 2016 to Oct 2016. Open submission discussions.

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Discussed in comments2 threads

  1. Microsoft’s Quantum Mechanics 1 comment

    • Quantum Machine Learning would be drastically sped up, as many linear operations could be done in log(N): http://arxiv.org/abs/1307.0411 A quantum machine learning computer could make scientific advances simply by processing large datasets at rate dramatically faster than humans…

      TTProgramsView on HNParent context

  2. How Quantum Computers and Machine Learning Will Revolutionize Big Data 1 comment

    • Lloyd's 2 recent papers are pretty interesting ... http://arxiv.org/abs/1307.0411 http://arxiv.org/abs/1307.0471

      jonbaerView on HNParent context

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