Optimal FALQON for Quantum Approximate Optimization via Layer-wise Parameter Tuning
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. This paper proposes Optimal FALQON, an optimization-based formulation that treats the per-layer time step ($δ_k$) and scaling factor ($M_k$) as decision variables optimized via classical methods. We present a comprehensive empirical study on all 94 non-isomorphic 3-regular graphs with 12 vertices, comparing Optima...
| Authors | Michael Mancini, Shabnam Sodagari |
|---|---|
| Primary category | quant-ph |
| Published | 8 May 2026 |
| Last updated at source | 7 July 2026 |
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