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Experimental demonstration of quantum learning speedup with classical input data

Title
Experimental demonstration of quantum learning speedup with classical input data
Author
이광걸
Keywords
ALGORITHM
Issue Date
2019-01
Publisher
AMER PHYSICAL SOC
Citation
PHYSICAL REVIEW A, v. 99, NO 1, No. 012313
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.
URI
https://journals.aps.org/pra/abstract/10.1103/PhysRevA.99.012313https://repository.hanyang.ac.kr/handle/20.500.11754/107240
ISSN
2469-9926; 2469-9934
DOI
10.1103/PhysRevA.99.012313
Appears in Collections:
COLLEGE OF NATURAL SCIENCES[S](자연과학대학) > PHYSICS(물리학과) > Articles
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