Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 이진형 | - |
dc.date.accessioned | 2019-12-10T03:56:33Z | - |
dc.date.available | 2019-12-10T03:56:33Z | - |
dc.date.issued | 2018-11 | - |
dc.identifier.citation | PHYSICAL REVIEW A, v. 98, no. 5, Article no. 052302 | en_US |
dc.identifier.issn | 2469-9926 | - |
dc.identifier.issn | 2469-9934 | - |
dc.identifier.uri | https://journals.aps.org/pra/abstract/10.1103/PhysRevA.98.052302 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/120670 | - |
dc.description.abstract | We propose a learning method for estimating unknown pure quantum states. The basic idea of our method is to learn a unitary operation (U) over cap that transforms a given unknown state vertical bar psi(tau)> to a known fiducial state vertical bar f >. Then, after completion of the learning process, we can estimate and reproduce vertical bar psi(tau)> based on the learned (U) over cap (a) under bar nd vertical bar f >. To realize this idea, we cast a random-based learning algorithm, called "single-shot measurement learning," in which the learning rule is based on an intuitive and reasonable criterion: the greater the number of success (or failure), the less (or more) changes are imposed. Remarkably, the learning process occurs by means of a single-shot measurement outcome. We demonstrate that our method works effectively, i.e., the learning is completed with a finite number, say N, of unknown-state copies. Most surprisingly, our method allows the maximum statistical accuracy to be achieved for large N, namely similar or equal to O (N-1) scales of average infidelity. It highlights a nontrivial message, that is, a random-based strategy can potentially be as accurate as other standard statistical approaches. | en_US |
dc.description.sponsorship | We are grateful to Jaewan Kim and Marcin Wiesniak for helpful discussions. J.B. was supported by the research project on quantum machine learning (No. 2018-104) of the ETRI affiliated research institute. S.M.L. and J.B. acknowledge the support of the R&D Convergence program of NST (National Research Council of Science and Technology) of Republic of Korea (No. CAP-18-08-KRISS). S.M.L. was also supported by KRISS projects (No. KRISS-2018-GP2018-0012, -0017). J.L. acknowledges the financial support of the Basic Science Research Program through the National Research Foundation of Korea (NRF) grant (No. 2014R1A2A1A10050117). | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | AMER PHYSICAL SOC | en_US |
dc.subject | TOMOGRAPHY | en_US |
dc.subject | OPTIMIZATION | en_US |
dc.subject | PROTOCOL | en_US |
dc.title | Learning unknown pure quantum states | en_US |
dc.type | Article | en_US |
dc.relation.no | 5 | - |
dc.relation.volume | 98 | - |
dc.identifier.doi | 10.1103/PhysRevA.98.052302 | - |
dc.relation.page | 523021-523028 | - |
dc.relation.journal | PHYSICAL REVIEW A | - |
dc.contributor.googleauthor | Lee, Sang Min | - |
dc.contributor.googleauthor | Lee, Jinhyoung | - |
dc.contributor.googleauthor | Bang, Jeongho | - |
dc.relation.code | 2018001337 | - |
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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