  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.
