Experimental demonstration of quantum learning speed-up with classical input data
Quantum Physics
2019-01-14 v3
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.
Cite
@article{arxiv.1706.01561,
title = {Experimental demonstration of quantum learning speed-up with classical input data},
author = {Joong-Sung Lee and Jeongho Bang and Sunghyuk Hong and Changhyoup Lee and Kang Hee Seol and Jinhyoung Lee and Kwang-Geol Lee},
journal= {arXiv preprint arXiv:1706.01561},
year = {2019}
}
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