  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.
