Investigating Training Dynamics of Quantum Generative Adversarial Networks

The recent advent of Quantum Generative Adversarial Networks (QGANs) has marked a significant milestone in the integration of quantum computing with deep learning. QuGANs, with their reduced parameter sets and quantum-state-based gradients, offer a promising solution to some of the traditional limitations faced by classical GANs, such as computational intensity and mode collapse. This work focuses on investigating the training dynamics of the discriminator within the QGAN framework, a component critical to the overall performance yet challenging in terms of training stability and efficiency. Additionally, I provide insights into the practical aspects of QGAN implementation, including training duration and computational resources, which are often overlooked in theoretical models.