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pith:2026:O752KUYLMWR3RQ4F2RH3IZKIHI
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Optimal FALQON for Quantum Approximate Optimization via Layer-wise Parameter Tuning

Michael Mancini, Shabnam Sodagari

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

C1strongest claim

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.

C2weakest assumption

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.

C3one line summary

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

Aliases

arxiv: 2605.08332 · arxiv_version: 2605.08332v2 · doi: 10.48550/arxiv.2605.08332 · pith_short_12: O752KUYLMWR3 · pith_short_16: O752KUYLMWR3RQ4F · pith_short_8: O752KUYL
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/O752KUYLMWR3RQ4F2RH3IZKIHI \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
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Canonical record JSON
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