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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Schuld, M., Sinayskiy, I., Petruccione, F. (2014). Quantum Computing for Pattern Classification. In: Pham, DN., Park, SB. (eds) PRICAI 2014: Trends in Artificial Intelligence. PRICAI 2014. Lecture Notes in Computer Science(), vol 8862. Springer, Cham. https://doi.org/10.1007/978-3-319-13560-1_17
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DOI: https://doi.org/10.1007/978-3-319-13560-1_17
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