Quantum Support Vector Machine for Big Data Classification
Name
PhysRevLett.113.130503.pdf
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129.39 KB
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Adobe PDF
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Author(s)
Mohseni, Masoud
Lloyd, Seth
Rebentrost, Frank Patrick
Date Issued
September 2014
Journal
Physical Review Letters
Publisher
American Physical Society
Citation
Rebentrost, Patrick, Masoud Mohseni, and Seth Lloyd. "Quantum Support Vector Machine for Big Data Classification." Phys. Rev. Lett. 113, 130503 (September 2014). © 2014 American Physical Society
Version
Final published version
Abstract
Supervised machine learning is the classification of new data based on already classified training examples. In this work, we show that the support vector machine, an optimized binary classifier, can be implemented on a quantum computer, with complexity logarithmic in the size of the vectors and the number of training examples. In cases where classical sampling algorithms require polynomial time, an exponential speedup is obtained. At the core of this quantum big data algorithm is a nonsparse matrix exponentiation technique for efficiently performing a matrix inversion of the training data inner-product (kernel) matrix.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology. Research Laboratory of Electronics
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1103/PhysRevLett.113.130503
