MLQAOA: Graph Learning Accelerated Hybrid Quantum-Classical Multilevel QAOA

Understand

Learning the problem structure at multiple levels of coarseness to inform the decomposition-based hybrid quantum-classical combinatorial optimization solvers is a promising approach to scaling up variational approaches.

• We introduce a multilevel algorithm reinforced with the spectral graph representation learning-based accelerator to tackle large-scale graph maximum cut instances and fused with several versions of the quantum approximate optimization algorithm (QAOA) and QAOA-inspired algorithms.
• The graph representation learning model utilizes the idea of QAOA variational parameters concentration and substantially improves the performance of QAOA.
• We demonstrate the potential of using multilevel QAOA and representation learning-based approaches on very large graphs by achieving high-quality solutions in a much faster time.