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EQuaTE: Efficient Quantum Train Engine for Dynamic Analysis via HCI-based Visual Feedback

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arxiv 2302.03853 v1 pith:ZLB6SRSW submitted 2023-02-08 quant-ph cs.SE

Soohyun Park , Won Joon Yun , Chanyoung Park , Youn Kyu Lee , Soyi Jung , Hao Feng , Joongheon Kim This is my paper · ORCID
classification quant-phcs.SE
keywords quantumequateanalysisbarrendynamicefficientenginefeedback
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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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