How Hard Is It to Train Variational Quantum Circuits?

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.

Intro
Toward Near-term Quantum Applications
Important Candidate Questions
Classical Neural Networks vs VQCs
Case Study II: Generative Models
Quantum GANS (LW18, etc) classical distributions
Robust Training of Quantum Generative Models
Compressing Quantum Circuits
Quantum Wasserstein Distance w/ regularization
Bonus : differentiable programming languages Deep Learning est mort. Vive Differentiable Programming