Exploring Differentiable Energy-Based Regularization in GANs with Quantum Computing Inspiration

This research explores a novel approach to improve Generative Adversarial Networks (GANs) using differentiable energy-based regularization, drawing inspiration from the Variational Quantum Eigensolver (VQE) algorithm. The paper's contribution lies in its application of quantum computing principles to enhance the performance and stability of GANs through auxiliary losses.

• •Applies quantum computing concepts to improve GAN performance.
• •Introduces VQE-inspired auxiliary losses for regularization.
• •Utilizes a simulator-based approach for exploration.