A strategy for quantum algorithm design assisted by machine learningopen access
- Authors
- Bang, Jeongho; Ryu, Junghee; Yoo, Seokwon; Pawlowski, Marcin; Lee, Jinhyoung
- Issue Date
- Jul-2014
- Publisher
- IOP PUBLISHING LTD
- Keywords
- quantum learning; quantum automatic control; quantum algorithm
- Citation
- NEW JOURNAL OF PHYSICS, v.16, pp.1 - 15
- Indexed
- SCIE
SCOPUS
- Journal Title
- NEW JOURNAL OF PHYSICS
- Volume
- 16
- Start Page
- 1
- End Page
- 15
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/143353
- DOI
- 10.1088/1367-2630/16/7/073017
- ISSN
- 1367-2630
- Abstract
- We propose a method for quantum algorithm design assisted by machine learning. The method uses a quantum-classical hybrid simulator, where a 'quantum student' is being taught by a 'classical teacher'. In other words, in our method, the learning system is supposed to evolve into a quantum algorithm for a given problem, assisted by a classical main-feedback system. Our method is applicable for designing quantum oracle-based algorithms. We chose, as a case study, an oracle decision problem, called a Deutsch-Jozsa problem. We showed by using Monte Carlo simulations that our simulator can faithfully learn a quantum algorithm for solving the problem for a given oracle. Remarkably, the learning time is proportional to the square root of the total number of parameters, rather than showing the exponential dependence found in the classical machine learning-based method.
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