AI based image generation Using QGAN and WGAN: A Comparative Perspective

Quantum Generative Adversarial Networks (QGANs) includes the principles of quantum computing and adversarial learning networks, giving the advantages of generative quality, parallelism, and state-space exploration. This paper presents a comparative analysis of FID between QGANs and Wasserstein GANs (WGANs), a variant of classical GAN for AI-based image generation. These two models are evaluated using MNIST data set for Fréchet Inception Distance (FID) and Inception Score (IS). Further, Structural Similarity Index Measure (SSIM), convergence time and robustness to mode collapse is also estimated. The Experimental results show that QGANs offer faster convergence and improved detail retention for images with low-dimensional quantum-enhanced simulations. QGAN implementations are constrained by the limitations of Noisy Intermediate-Scale Quantum (NISQ) hardware, the outcome suggests promising future potential for QGANs in scalable AI-based image generation.