Citation Export
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Park, Soohyun | - |
| dc.contributor.author | Feng, Hao | - |
| dc.contributor.author | Yun, Won Joon | - |
| dc.contributor.author | Park, Chanyoung | - |
| dc.contributor.author | Lee, Youn Kyu | - |
| dc.contributor.author | Jung, Soyi | - |
| dc.contributor.author | Kim, Joongheon | - |
| dc.date.issued | 2023-01-01 | - |
| dc.identifier.uri | https://aurora.ajou.ac.kr/handle/2018.oak/36951 | - |
| dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85174663769&origin=inward | - |
| dc.description.abstract | This paper proposes an efficient quantum train engine (EQuaTE), a novel tool for quantum machine learning software which plots gradient variances to check whether our quantum neural network (QNN) falls into local minima (called barren plateaus in QNN). EQuaTE can be realized via dynamic analysis of the undetermined probabilistic qubit states. Furthermore, the proposed EQuaTE is capable of HCI-based visual feedback such that software engineers can recognize barren plateaus via visualization, allowing the modification of QNN based on this information. | - |
| dc.description.sponsorship | V. CONCLUDINGREMARKS This paper proposes a novel QML software tool which visually plots gradient variances to check for the occurrence of barren plateaus via dynamic analysis, called EQuaTE. Furthermore, our EQuaTE is capable for visual feedback because it allows software engineers to modify QNNs based on the recognized barren plateaus using tensorboard. Acknowledgement. This work was supported by RF-Korea (2022R1A2C2004869). (Corresponding author: Joongheon Kim, joongheon@korea.ac.kr). | - |
| dc.language.iso | eng | - |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
| dc.subject.mesh | Dynamic softwares | - |
| dc.subject.mesh | Dynamics analysis | - |
| dc.subject.mesh | Engine design | - |
| dc.subject.mesh | Gradient variance | - |
| dc.subject.mesh | Local minimums | - |
| dc.subject.mesh | Machine learning software | - |
| dc.subject.mesh | Probabilistics | - |
| dc.subject.mesh | Quantum machines | - |
| dc.subject.mesh | Quantum neural networks | - |
| dc.subject.mesh | Software analysis | - |
| dc.title | Demo: EQuaTE: Efficient Quantum Train Engine Design and Demonstration for Dynamic Software Analysis | - |
| dc.type | Conference | - |
| dc.citation.conferenceDate | 2023.7.18. ~ 2023.7.21. | - |
| dc.citation.conferenceName | 43rd IEEE International Conference on Distributed Computing Systems, ICDCS 2023 | - |
| dc.citation.edition | Proceedings - 2023 IEEE 43rd International Conference on Distributed Computing Systems, ICDCS 2023 | - |
| dc.citation.endPage | 1012 | - |
| dc.citation.startPage | 1009 | - |
| dc.citation.title | Proceedings - International Conference on Distributed Computing Systems | - |
| dc.citation.volume | 2023-July | - |
| dc.identifier.bibliographicCitation | Proceedings - International Conference on Distributed Computing Systems, Vol.2023-July, pp.1009-1012 | - |
| dc.identifier.doi | 10.1109/icdcs57875.2023.00116 | - |
| dc.identifier.scopusid | 2-s2.0-85174663769 | - |
| dc.type.other | Conference Paper | - |
| dc.description.isoa | false | - |
| dc.subject.subarea | Software | - |
| dc.subject.subarea | Hardware and Architecture | - |
| dc.subject.subarea | Computer Networks and Communications | - |
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