pith:LMFZ7Z5Z
Spectral Gap Informed Ramp QAOA
Spectral gap informed ramps improve QAOA performance over linear schedules on Grover's problem and maximum independent set.
arxiv:2604.24580 v2 · 2026-04-27 · quant-ph
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Claims
SGIR-QAOA has performance improvements over LR-QAOA on Grover's problem at constant depth and requires shorter depths to achieve the same optimal solution probability; these benefits extend to MIS and persist under mild depolarising noise when using extrapolated spectral gaps.
That spectral gap information from the chosen adiabatic Hamiltonian (with QAOA mixer as initial Hamiltonian) produces ramps that remain effective when extrapolated to scales where exact gaps cannot be computed, and that the observed improvements are not artifacts of the specific problem instances or noise model tested.
SGIR-QAOA uses spectral gap information to create non-linear parameter schedules that outperform linear ramps on Grover's problem and MIS, achieving target probabilities at lower depths even under mild noise.
Receipt and verification
| First computed | 2026-06-03T01:05:50.666207Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
5b0b9fe7b99cb69002524d443975f15d37388b323a8d3269662ffebb77372ac9
Aliases
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/LMFZ7Z5ZTS3JAASSJVCDS5PRLU \
| 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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