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arXiv · 1706.01561

Experimental demonstration of quantum learning speed-up with classical input data

Joong-Sung Lee·Jeongho Bang·Sunghyuk Hong·Changhyoup Lee·Kang Hee Seol·Jinhyoung Lee·Kwang-Geol Lee
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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 binary classification task. Our experimental machine exhibits quantum learning speed-up of approximately 36%, as compared to the fully classical machine. In addition, it features strong robustness against dephasing noise.

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Joong-Sung Lee, Jeongho Bang, Sunghyuk Hong, Changhyoup Lee, Kang Hee Seol, Jinhyoung Lee, Kwang-Geol Lee. 2017-06-05. Experimental demonstration of quantum learning speed-up with classical input data. https://doi.org/10.1103/physreva.99.012313

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