A quantum speedup in machine learning: finding an N-bit Boolean function for a classificationopen access
- Authors
- Yoo, Seokwon; Bang, Jeongho; Lee, Changhyoup; Lee, Jinhyoung
- Issue Date
- Oct-2014
- Publisher
- IOP PUBLISHING LTD
- Keywords
- quantum information; quantum learning; machine learning
- Citation
- NEW JOURNAL OF PHYSICS, v.16, pp.1 - 16
- Indexed
- SCIE
SCOPUS
- Journal Title
- NEW JOURNAL OF PHYSICS
- Volume
- 16
- Start Page
- 1
- End Page
- 16
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/143310
- DOI
- 10.1088/1367-2630/16/10/103014
- ISSN
- 1367-2630
- Abstract
- We compare quantum and classical machines designed for learning an N-bit Boolean function in order to address how a quantum system improves the machine learning behavior. The machines of the two types consist of the same number of operations and control parameters, but only the quantum machines utilize the quantum coherence naturally induced by unitary operators. We show that quantum superposition enables quantum learning that is faster than classical learning by expanding the approximate solution regions, i.e., the acceptable regions. This is also demonstrated by means of numerical simulations with a standard feedback model, namely random search, and a practical model, namely differential evolution.
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