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Demo: EQuaTE: Efficient Quantum Train Engine Design and Demonstration for Dynamic Software Analysis
- Authors
- Park, Soohyun; Feng, Hao; Yun, Won Joon; Park, Chanyoung; Lee, Youn Kyu; Jung, Soyi; Kim, Joongheon
- Issue Date
- 2023
- Publisher
- IEEE COMPUTER SOC
- Citation
- 2023 IEEE 43RD INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS, ICDCS, v.2023-July, pp 1009 - 1012
- Pages
- 4
- Journal Title
- 2023 IEEE 43RD INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS, ICDCS
- Volume
- 2023-July
- Start Page
- 1009
- End Page
- 1012
- ISSN
- 1063-6927
- 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.
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Related Researcher
- Lee, Youn Kyu
- Engineering (Department of Computer Engineering)