Quantum Webinar: Enhancing QAOA with Machine Learning for Adaptive Circuits

This educational talk presents recent advancements in the Quantum Approximate Optimization Algorithm (QAOA) for training quantum neural networks (QNNs). It introduces a fully quantum training framework that integrates adaptive mixers, phase oracles, and classical machine learning methods such as recurrent neural networks (RNNs) and reinforcement learning (RL) to dynamically optimize QAOA layers. The session covers the mathematical foundations of QAOA, its Hamiltonian formulation, and the connection to adiabatic evolution, while addressing challenges like barren plateaus and shallow circuits.