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journal art of quantum computing

Nature, 14 March 2019

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Machine learning and quantum computing have the potential to solve previously untenable problems. In machine learning, techniques such as pattern classification work well — for example, categorizing photographic images — but can run into problems when the structure of the data becomes too complex. The common approach of mapping such complex data to a high dimensional space where data points can be analysed on the basis of their most essential features requires a level of computing power that is difficult to attain using classical machines. In this week’s issue, Kristan Temme and his colleagues show that mapping the objects for classification to quantum state space for feature analysis could help to overcome this limitation. By running two quantum algorithms experimentally, the researchers show that harnessing the processing power of a quantum computer has the potential to offer a net advantage for machine learning involving large-scale classification tasks. 

"Supervised learning with quantum-enhanced feature spaces." 
Vojtěch Havlíček, Antonio D. Córcoles, Kristan Temme, Aram W. Harrow, Abhinav Kandala, Jerry M. Chow , and Jay M. Gambetta 
Nature, Volume 567, pages 209–212 (2019)

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