Quantum Approximate Optimization Algorithm (QAOA) - Recent Advances and Performance Updates

Watch a technical lecture exploring the Quantum Approximate Optimization Algorithm (QAOA), delving into its design for solving combinatorial optimization problems through alternating unitary transformations. Learn about proven worst-case performance guarantees for MaxCut and the Sherrington Kirkpatrick model, with established QAOA performance up to depth 20 on typical instances in the infinite size limit. Discover how QAOA demonstrates quantum supremacy at its shallowest depth, both in worst-case scenarios and typical SK model instances. Examine performance obstacles related to locality and the Overlap Gap property, which only become relevant at depth log n with millions of qubits. Explore recent findings showing that for MaxCut on 3-regular graphs, instance-independent parameters can be pre-selected to work effectively on all instances at high sizes, eliminating the need for parameter optimization during algorithm execution.