Lecture
This lecture by the instructor covers the applications of neural quantum states in many-body quantum systems, including autoregressive quantum states and frustrated spins. It also discusses mapping techniques like Jordan-Wigner and Bravyi-Kitaev mappings, as well as simulating quantum circuits and estimating loss functions. The lecture delves into the accuracy and efficiency improvements brought by autoregressive models and deeper neural networks, showcasing results from various research papers. Furthermore, it explores the trade-off between variance and bias in quantum computations, along with noise analysis and simulating quantum approximate optimization algorithms. The presentation concludes with benchmarking small and large quantum circuits, demonstrating the continuous improvements in neural quantum states.