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Quantum Machine Learning
Quantum computing for pattern classification
arXiv
Authors: Maria Schuld, Ilya Sinayskiy, Francesco Petruccione
Year
2014
Paper ID
45974
Status
Preprint
Abstract Read
~2 min
Abstract Words
94
Citations
N/A
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.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2014 reference point for readers tracking recent quantum research.
- It is well known that for certain tasks, quantum computing outperforms classical computing.
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