2014/12/11 by Maria Schuld, Ilya Sinayskiy, Francesco Petruccione · 1 citation
Physics and Astronomy · #quant-ph
published as Trends in Artificial Intelligence, LNAI 8862, Springer, pp. 208--220 (2014) · 14 pages, 3 figures, presented at the 13th Pacific Rim International Conference on Artificial Intelligence
arxiv created 2014/12/11 · arxiv updated 2014/12/12
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.