Experimental demonstration of quantum learning speedup with classical input data

Joong Sung Lee, Jeongho Bang, Sunghyuk Hong, Changhyoup Lee, Kang Hee Seol, Jinhyoung Lee, Kwang Geol Lee

Research output: Contribution to journalArticleResearchpeer-review

Abstract

We consider quantum-classical hybrid machine learning in which large-scale input channels remain classical and small-scale working channels process quantum operations conditioned on classical input data. This does not require the conversion of classical (big) data to a quantum superposed state, in contrast to recently developed approaches for quantum machine learning. We performed optical experiments to illustrate a single-bit universal machine, which can be extended to a large-bit circuit for a binary classification task. Our experimental machine exhibits quantum learning speedup of approximately 36%, as compared with the fully classical machine. In addition, it features strong robustness against dephasing noise.

Original languageEnglish
Article number012313
JournalPhysical Review A
Volume99
Issue number1
DOIs
StatePublished - 2019 Jan 10

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Lee, Joong Sung ; Bang, Jeongho ; Hong, Sunghyuk ; Lee, Changhyoup ; Seol, Kang Hee ; Lee, Jinhyoung ; Lee, Kwang Geol. / Experimental demonstration of quantum learning speedup with classical input data. In: Physical Review A. 2019 ; Vol. 99, No. 1.
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Experimental demonstration of quantum learning speedup with classical input data. / Lee, Joong Sung; Bang, Jeongho; Hong, Sunghyuk; Lee, Changhyoup; Seol, Kang Hee; Lee, Jinhyoung; Lee, Kwang Geol.

In: Physical Review A, Vol. 99, No. 1, 012313, 10.01.2019.

Research output: Contribution to journalArticleResearchpeer-review

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