A Novel XAI-Enhanced Quantum Adversarial Networks for Velocity Dispersion Modeling in MaNGA Galaxies

TL;DR

This paper introduces a quantum adversarial framework combining quantum neural networks with classical deep learning and interpretability tools, achieving improved accuracy and explainability in galaxy velocity dispersion modeling.

Contribution

It presents a novel hybrid quantum-classical adversarial model with LIME-based interpretability and quantum GAN extensions, enhancing QML's accuracy and transparency.

Findings

Achieved RMSE = 0.27 and R^2 = 0.59 on galaxy data

Demonstrated improved performance over other adversarial models

Showcased the potential of quantum-inspired methods for interpretable predictions

Abstract

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.