EQuaTE: Efficient Quantum Train Engine for Dynamic Analysis via HCI-based Visual Feedback
Quantum Physics
2023-02-09 v1 Software Engineering
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.
Cite
@article{arxiv.2302.03853,
title = {EQuaTE: Efficient Quantum Train Engine for Dynamic Analysis via HCI-based Visual Feedback},
author = {Soohyun Park and Won Joon Yun and Chanyoung Park and Youn Kyu Lee and Soyi Jung and Hao Feng and Joongheon Kim},
journal= {arXiv preprint arXiv:2302.03853},
year = {2023}
}
Comments
5 pages, 5 figures