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arXiv · 1412.3646

Quantum computing for pattern classification

Maria Schuld·Ilya Sinayskiy·Francesco Petruccione
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Abstract

It is well known that for certain tasks, quantum computing outperforms classical computing. A growing number of contributions try to use this advantage in order to improve or extend classical machine learning algorithms by methods of quantum information theory. This paper gives a brief introduction into quantum machine learning using the example of pattern classification. We introduce a quantum pattern classification algorithm that draws on Trugenberger's proposal for measuring the Hamming distance on a quantum computer (CA Trugenberger, Phys Rev Let 87, 2001) and discuss its advantages using handwritten digit recognition as from the MNIST database.

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Maria Schuld, Ilya Sinayskiy, Francesco Petruccione. 2014-12-11. Quantum computing for pattern classification. https://arxiv.org/abs/1412.3646

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