The paper benchmarks approximation techniques and transfer learning for setting QAOA angles at utility scale and extracts operational guidance from hardware-validated results.
Conditional diffusion- based parameter generation for quantum approximate optimization al- gorithm
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Swarm methods such as PSO, FIPSO, and QPSO yield lower approximation gaps and more stable convergence than Adam, COBYLA, or SPSA when tuning QAOA parameters on weighted MaxCut instances, especially under noise and limited shots.
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Setting angles in quantum approximate optimization at utility-scale
The paper benchmarks approximation techniques and transfer learning for setting QAOA angles at utility scale and extracts operational guidance from hardware-validated results.
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Benchmarking Swarm Optimization Algorithms for Parameter Initialization in the Quantum Approximate Optimization Algorithm
Swarm methods such as PSO, FIPSO, and QPSO yield lower approximation gaps and more stable convergence than Adam, COBYLA, or SPSA when tuning QAOA parameters on weighted MaxCut instances, especially under noise and limited shots.