AI RESEARCH
Optimal FALQON for Quantum Approximate Optimization via Layer-wise Parameter Tuning
arXiv CS.AI
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ArXi:2605.08332v1 Announce Type: cross Feedback-based adaptive quantum optimization (FALQON) is a promising approach for solving combinatorial problems on noisy intermediate-scale quantum (NISQ) devices, requiring only single circuit evaluations per layer. However, standard FALQON relies on fixed hyperparameters that severely limit convergence speed, requiring hundreds to thousands of layers for acceptable solutions.