Experimental realization of a quantum support vector machine

The fundamental principle of artificial intelligence is the ability of machines to learn from previous experience and do future work accordingly. In the age of big data, classical learning machines often require huge computational resources in many practical cases. Quantum machine learning algorithm...

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Veröffentlicht in:Physical review letters 2015-04, Vol.114 (14), p.140504-140504, Article 140504
Hauptverfasser: Li, Zhaokai, Liu, Xiaomei, Xu, Nanyang, Du, Jiangfeng
Format: Artikel
Sprache:eng
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Zusammenfassung:The fundamental principle of artificial intelligence is the ability of machines to learn from previous experience and do future work accordingly. In the age of big data, classical learning machines often require huge computational resources in many practical cases. Quantum machine learning algorithms, on the other hand, could be exponentially faster than their classical counterparts by utilizing quantum parallelism. Here, we demonstrate a quantum machine learning algorithm to implement handwriting recognition on a four-qubit NMR test bench. The quantum machine learns standard character fonts and then recognizes handwritten characters from a set with two candidates. Because of the wide spread importance of artificial intelligence and its tremendous consumption of computational resources, quantum speedup would be extremely attractive against the challenges of big data.
ISSN:0031-9007
1079-7114
DOI:10.1103/PhysRevLett.114.140504