Explore the challenges of training variational quantum circuits in this 29-minute lecture by Xiaodi Wu from the University of Maryland. Delve into the comparison between classical neural networks and variational quantum circuits, examining their applications in near-term quantum computing. Investigate important candidate questions and case studies, focusing on generative models such as Quantum GANs. Learn about robust training techniques for quantum generative models, methods for compressing quantum circuits, and the concept of quantum Wasserstein distance with regularization. Gain insights into differentiable programming languages and their potential impact on the future of deep learning and quantum computing.