用于模型可解释性的量子梯度类激活图
Quantum Gradient Class Activation Map for Model Interpretability
AI总结：
针对量子机器学习可解释性研究不足的问题，提出量子梯度类激活图（QGrad-CAM）框架，结合量子与经典计算优势推导特征图重要性公式，在多数据集上生成优质可视化解释。
摘要：
量子机器学习（QML）近期在多个领域取得显著进展。尽管成果颇丰，QML应用的安全性与可解释性尚未得到充分研究。本研究提出利用变分量子线路（VQCs）进行激活映射以提升模型透明度，并引入量子梯度类激活图（QGrad-CAM）。这一量子-经典混合计算框架兼具量子与经典计算的优势，可推导得出特征图重要性的显式公式。实验结果表明，该方法在图像与语音数据集上均能生成显著、细粒度且具备类别区分性的可视化解释。
英文摘要：
Quantum machine learning (QML) has recently made significant advancements in various topics. Despite the successes, the safety and interpretability of QML applications have not been thoroughly investigated. This work proposes using Variational Quantum Circuits (VQCs) for activation mapping to enhance model transparency, introducing the Quantum Gradient Class Activation Map (QGrad-CAM). This hybrid quantum-classical computing framework leverages both quantum and classical strengths and gives access to the derivation of an explicit formula of feature map importance. Experimental results demonstrate significant, fine-grained, class-discriminative visual explanations generated across both image and speech datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
