Demo: EQuaTE: Efficient Quantum Train Engine Design and Demonstration for Dynamic Software Analysis

Soohyun Park, Hao Feng, Won Joon Yun, Chanyoung Park, Youn Kyu Lee, Soyi Jung, Joongheon Kim

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.