pith:O752KUYL
Optimal FALQON for Quantum Approximate Optimization via Layer-wise Parameter Tuning
Optimizing per-layer time steps and scaling factors in FALQON improves success probability and efficiency for combinatorial optimization on quantum devices.
arxiv:2605.08332 v2 · 2026-05-08 · quant-ph · cs.AI
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Claims
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.
That classical optimization of the per-layer parameters δ_k and M_k can be performed efficiently and that the observed improvements on 12-vertex 3-regular graphs will generalize to larger or differently structured instances without prohibitive overhead.
Optimal FALQON optimizes per-layer δ_k and M_k via classical methods, yielding statistically significant gains in success probability and efficiency over standard FALQON on 94 non-isomorphic 3-regular graphs with 12 vertices.
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| First computed | 2026-07-09T00:19:14.140853Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
77fba5530b65a3b8c385d44fb465483a0257022352faba35f6a6bf9ca08b28f5
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/O752KUYLMWR3RQ4F2RH3IZKIHI \
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Canonical record JSON
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