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

• URL: http://arxiv.org/abs/2510.24598v1
• Date: Tue, 28 Oct 2025 16:27:10 GMT
• Title: A Novel XAI-Enhanced Quantum Adversarial Networks for Velocity Dispersion Modeling in MaNGA Galaxies
• Authors: Sathwik Narkedimilli, N V Saran Kumar, Aswath Babu H, Manjunath K Vanahalli, Manish M, Vinija Jain, Aman Chadha,
• Abstract summary: We propose a novel quantum adversarial framework that integrates a hybrid quantum neural network (QNN) with classical deep learning layers.<n>In the proposed model, an adversarial evaluator concurrently guides the QNN by computing feedback loss, thereby optimizing both prediction accuracy and model explainability.<n> Empirical evaluations show that the Vanilla model achieves RMSE = 0.27, MSE = 0.071, MAE = 0.21, and R2 = 0.59, delivering the most consistent performance across regression metrics compared to adversarial counterparts.
• Score: 14.016108312641101
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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