A Tutorial on Quantum Graph Recurrent Neural Network (QGRNN)
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choi, J. | - |
dc.contributor.author | Oh, S. | - |
dc.contributor.author | Kim, J. | - |
dc.date.accessioned | 2023-03-08T12:57:20Z | - |
dc.date.available | 2023-03-08T12:57:20Z | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 1976-7684 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/63118 | - |
dc.description.abstract | Over the past decades, various neural networks have been proposed with the rapid development of the machine learning field. In particular, graph neural networks using feature-vectors assigned to nodes and edges have been attracting attention in various fields. The usefulness of graph neural networks also affected the field of quantum computing, which led to the birth of quantum graph neural networks composed of parameterized quantum circuits. The quantum graph neural networks have many possibilities as applications from the simulation perspective of quantum dynamics. Among the application models of various quantum graph neural networks, the quantum graph recurrent neural network (QGRNN) is proven to be effective in training the Ising model Hamiltonian. Thus, this paper introduces the concepts of the Ising model, variational quantum eigensolver (VQE) for preparing quantum data, and QGRNN from a software engineer's point of view. © 2021 IEEE. | - |
dc.format.extent | 4 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | IEEE Computer Society | - |
dc.title | A Tutorial on Quantum Graph Recurrent Neural Network (QGRNN) | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/ICOIN50884.2021.9333917 | - |
dc.identifier.bibliographicCitation | International Conference on Information Networking, v.2021-January, pp 46 - 49 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000657974100013 | - |
dc.identifier.scopusid | 2-s2.0-85100797645 | - |
dc.citation.endPage | 49 | - |
dc.citation.startPage | 46 | - |
dc.citation.title | International Conference on Information Networking | - |
dc.citation.volume | 2021-January | - |
dc.type.docType | Proceedings Paper | - |
dc.subject.keywordAuthor | Ising Model | - |
dc.subject.keywordAuthor | QGRNN | - |
dc.subject.keywordAuthor | VQE | - |
dc.subject.keywordPlus | Ising model | - |
dc.subject.keywordPlus | Quantum computers | - |
dc.subject.keywordPlus | Quantum theory | - |
dc.subject.keywordPlus | Application models | - |
dc.subject.keywordPlus | Feature vectors | - |
dc.subject.keywordPlus | Graph neural networks | - |
dc.subject.keywordPlus | Model Hamiltonians | - |
dc.subject.keywordPlus | Quantum circuit | - |
dc.subject.keywordPlus | Quantum Computing | - |
dc.subject.keywordPlus | Quantum dynamics | - |
dc.subject.keywordPlus | Quantum graph | - |
dc.subject.keywordPlus | Recurrent neural networks | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
84, Heukseok-ro, Dongjak-gu, Seoul, Republic of Korea (06974)02-820-6194
COPYRIGHT 2019 Chung-Ang University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.