Experimental demonstration of quantum learning speedup with classical input data
- Authors
- Lee, Joong-Sung; Bang, Jeongho; Hong, Sunghyuk; Lee, Changhyoup; Seol, Kang Hee; Lee, Jinhyoung; Lee, Kwang-Geol
- Issue Date
- Jan-2019
- Publisher
- AMER PHYSICAL SOC
- Citation
- PHYSICAL REVIEW A, v.99, no.1, pp.1 - 9
- Indexed
- SCIE
SCOPUS
- Journal Title
- PHYSICAL REVIEW A
- Volume
- 99
- Number
- 1
- Start Page
- 1
- End Page
- 9
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/148538
- DOI
- 10.1103/PhysRevA.99.012313
- ISSN
- 2469-9926
- 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.
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