Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 이진형 | - |
dc.date.accessioned | 2018-03-15T01:15:03Z | - |
dc.date.available | 2018-03-15T01:15:03Z | - |
dc.date.issued | 2014-07 | - |
dc.identifier.citation | New Journal of Physics, 2014, 16, P.1-14 | en_US |
dc.identifier.issn | 1367-2630 | - |
dc.identifier.uri | http://iopscience.iop.org/article/10.1088/1367-2630/16/7/073017/meta | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11754/46902 | - |
dc.description.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. | en_US |
dc.description.sponsorship | JB would like to thank M Zukowski, H J Briegel, and B C Sanders for discussions and comments. We acknowledge the financial support of National Research Foundation of Korea (NRF) grants funded by the Korea government (MEST; No. 2010-0018295 and No. 2010-0015059). JR and MP were supported by the Foundation for Polish Science TEAM project cofinanced by the EU European Regional Development Fund. JR was also supported by NCBiR-CHIST-ERA Project QUASAR. MP was also supported by the UK EP-SRC and ERC grant QOLAPS. | en_US |
dc.language.iso | en | en_US |
dc.publisher | IOP Publishing LTD | en_US |
dc.subject | quantum learning | en_US |
dc.subject | quantum automatic control | en_US |
dc.subject | quantum algorithm | en_US |
dc.title | A strategy for quantum algorithm design assisted by machine learning | en_US |
dc.type | Article | en_US |
dc.relation.volume | 16 | - |
dc.identifier.doi | 10.1088/1367-2630/16/7/073017 | - |
dc.relation.page | 73017-73017 | - |
dc.relation.journal | NEW JOURNAL OF PHYSICS | - |
dc.contributor.googleauthor | Bang, Jeongho | - |
dc.contributor.googleauthor | Ryu, Junghee | - |
dc.contributor.googleauthor | Yoo, Seokwon | - |
dc.contributor.googleauthor | Pawlowski, Marcin | - |
dc.contributor.googleauthor | Lee, Jinhyoung | - |
dc.relation.code | 2014036700 | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF NATURAL SCIENCES[S] | - |
dc.sector.department | DEPARTMENT OF PHYSICS | - |
dc.identifier.pid | hyoung | - |
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