Symmetry reductions in QAOA for MaxCut can collapse DLA dimensions from exponential to quadratic depending on the fixed variable, with graph embeddings ensuring expressivity and improved trainability.
Graph representation learning for parameter transfer- ability in quantum approximate optimization algorithm.Quantum Machine Intelligence, 6(2):46, 2024
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Reductions of QAOA Induced by Classical Symmetries: Theoretical Insights and Practical Implications
Symmetry reductions in QAOA for MaxCut can collapse DLA dimensions from exponential to quadratic depending on the fixed variable, with graph embeddings ensuring expressivity and improved trainability.