A Novel XAI-Enhanced Quantum Adversarial Networks for Velocity Dispersion Modeling in MaNGA Galaxies
This work addresses the problem of developing interpretable and high-performance quantum machine learning models for astrophysics researchers, but it appears incremental as it combines existing quantum and classical methods with adversarial techniques.
The paper tackled the challenge of balancing predictive accuracy, robustness, and interpretability in quantum machine learning by proposing a quantum adversarial framework integrating hybrid quantum neural networks with classical layers and LIME-based interpretability. The model achieved RMSE = 0.27, MSE = 0.071, MAE = 0.21, and R^2 = 0.59, showing consistent performance in velocity dispersion modeling for MaNGA galaxies.
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.