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Quantum Support Vector Machine for Big Data Classification

Author(s)
Mohseni, Masoud; Lloyd, Seth; Rebentrost, Frank Patrick
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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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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.
Date issued
2014-09
URI
http://hdl.handle.net/1721.1/90391
Department
Massachusetts Institute of Technology. Department of Mechanical Engineering; Massachusetts Institute of Technology. Research Laboratory of Electronics
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
ISSN
0031-9007
1079-7114

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