Presentation
Invited: Graph Learning for Parameter Prediction of Quantum Approximate Optimization Algorithm
DescriptionIn recent years, the importance of quantum computing has been increasingly recognized in the field of combinatorial optimization problems. Despite their importance, the application of the Quantum Approximate Optimization Algorithm (QAOA) in efficiently solving the Max-Cut problem remains challenges, primarily hindered by the limitations of available quantum computing resources. To address this challenge, we focus on optimizing initialization methods and extending techniques from basic unweighted Max-cut problems to more intricate weighted problems. We incorporate the Graph Neural Network (GNN) as a warm-start technique for initializing QAOA parameters. This approach significantly reduces the quantum computing resource overhead, thereby enhancing QAOA's capability to tackle sophisticated weighted optimization problems.
Event Type
Special Session (Research)
TimeTuesday, June 252:00pm - 2:30pm PDT
Location3006, 3rd Floor
AI