Abstract: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 ($\delta_k$) 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 Optimal FALQON with standard FALQON and multiple QAOA variants. Results demonstrate statistically significant improvements in success probability, evaluation efficiency, and depth-normalized cost across the evaluated benchmarks. Furthermore, initializing QAOA with parameters from Optimal FALQON yields superior warm-start performance compared to fixed initialization.
| Subjects: | Quantum Physics (quant-ph); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.08332 [quant-ph] |
| (or arXiv:2605.08332v1 [quant-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2605.08332 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Shabnam Sodagari [view email]
[v1]
Fri, 8 May 2026 17:55:49 UTC (152 KB)