量子对抗网络提升星系速度弥散建模的准确性与可解释性
A Novel XAI-Enhanced Quantum Adversarial Networks for Velocity Dispersion Modeling in MaNGA Galaxies
- 融合量子神经网络与经典深度学习，用可解释性评估器引导优化
- 基准模型在多个指标上表现最佳，RMSE为0.27，R²达0.59
- 适合关注量子机器学习可解释性的天体物理与交叉领域研究者
当前量子机器学习方法在预测精度、鲁棒性与可解释性之间难以平衡。为此，我们提出一种新型量子对抗框架，将混合量子神经网络（QNN）与经典深度学习层结合，由基于LIME的可解释性评估器引导，并扩展至量子GAN和自监督变体。模型中，对抗评估器通过计算反馈损失同时优化预测准确性和模型可解释性。实验表明，基础模型在回归指标上表现最优：RMSE = 0.27，MSE = 0.071，MAE = 0.21，R² = 0.59，性能最稳定。结果证明，将量子启发方法与经典架构结合，有助于构建轻量、高性能且可解释的预测模型，推动量子机器学习突破现有局限。
原文摘要 · Abstract (English)
Current quantum machine learning approaches often face challenges balancing predictive accuracy, robustness, and interpretability. To address this, we propose a novel quantum adversarial framework that integrates a hybrid quantum neural network (QNN) with classical deep learning layers, guided by an evaluator model with LIME-based interpretability, and extended through quantum GAN and self-supervised variants. In the proposed model, an adversarial evaluator concurrently guides the QNN by computing feedback loss, thereby optimizing both prediction accuracy and model explainability. Empirical evaluations show that the Vanilla model achieves RMSE = 0.27, MSE = 0.071, MAE = 0.21, and R^2 = 0.59, delivering the most consistent performance across regression metrics compared to adversarial counterparts. These results demonstrate the potential of combining quantum-inspired methods with classical architectures to develop lightweight, high-performance, and interpretable predictive models, advancing the applicability of QML beyond current limitations.
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