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EQuaTE: Efficient Quantum Train Engine for Dynamic Analysis via HCI-based Visual Feedback
February 08, 2023 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Soohyun Park, Won Joon Yun, Chanyoung Park, Youn Kyu Lee, Soyi Jung, Hao Feng, Joongheon Kim
arXiv ID
2302.03853
Category
quant-ph: Quantum Computing
Cross-listed
cs.SE
Citations
1
Venue
arXiv.org
Last Checked
5 months ago
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). This can be realized via dynamic analysis due to undetermined probabilistic qubit states. Furthermore, our EQuaTE is capable for HCI-based visual feedback because software engineers can recognize barren plateaus via visualization; and also modify QNN based on this information.
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