Demo: EQuaTE: Efficient Quantum Train Engine Design and Demonstration for Dynamic Software Analysis - Park, Soohyun; Feng, Hao; Yun, Won Joon; Park, Chanyoung; Lee, Youn Kyu; Jung, Soyi; Kim, Joongheon (Proceedings - International Conference on Distributed Computing Systems, 2023)
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.
Keywords: Dynamic softwares; Dynamics analysis; Engine design; Gradient variance; Local minimums; Machine learning software; Probabilistics; Quantum machines; Quantum neural networks; Software analysis