In this paper, we present a comprehensive empirical study on the convergence and variability of Quantum Generative Adversarial Networks (QGANs) compared to classical Generative Adversarial Networks (GANs) for advanced cybersecurity applications. Our methodology leverages both real-life and GAN-generated synthetic datasets to systematically assess loss function stability across extensive training epochs. By integrating Quantum-inspired architectures with classical discriminators, our proposed framework enables a fine-grained investigation of Generator–Discriminator dynamics and entropy-based stability metrics. Experimental findings highlight that QGANs consistently achieve lower and more stable generator loss values than traditional GANs, demonstrating enhanced robustness and reliability for cybersecurity tasks.