A strategy for quantum algorithm design assisted by machine learning
- Title
- A strategy for quantum algorithm design assisted by machine learning
- Author
- 이진형
- Keywords
- quantum learning; quantum automatic control; quantum algorithm
- Issue Date
- 2014-07
- Publisher
- IOP Publishing LTD
- Citation
- New Journal of Physics, 2014, 16, P.1-14
- 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.
- URI
- http://iopscience.iop.org/article/10.1088/1367-2630/16/7/073017/metahttp://hdl.handle.net/20.500.11754/46902
- ISSN
- 1367-2630
- DOI
- 10.1088/1367-2630/16/7/073017
- Appears in Collections:
- COLLEGE OF NATURAL SCIENCES[S](자연과학대학) > PHYSICS(물리학과) > Articles
- Files in This Item:
- Bang_2014_New_J._Phys._16_073017.pdfDownload
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