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.

翻译：本文提出了一种高效量子训练引擎（EQuaTE），这是一种用于量子机器学习软件的新型工具，通过绘制梯度方差图来检测量子神经网络（QNN）是否陷入局部最小值（在QNN中称为贫瘠高原）。由于量子比特状态具有不确定的概率性，该检测可通过动态分析实现。此外，EQuaTE具备基于人机交互（HCI）的视觉反馈能力：软件工程师可通过可视化识别贫瘠高原，并据此修改量子神经网络。