Machine Learning in Quantum Science and Computing

This lecture by the instructor covers the application of machine learning techniques in quantum science and quantum computing, focusing on the challenges and advancements in understanding quantum many-body systems. The lecture delves into the mathematical foundations of quantum physics, the variational formulation as a solution approach, and the use of neural-network quantum states. It also explores the ground-state search problem, variational sampling methods, and the concept of variational quantum eigensolvers. The lecture concludes with an overview of the NetKet project, a machine learning toolkit for many-body quantum systems, and discusses the complexities and challenges in learning quantum states, especially in fermionic systems.