A GNN predicts Gaussians over QAOA parameters to create graph-conditioned trust regions that reduce circuit evaluations for MaxCut from 85-343 down to 45 while keeping approximation ratios within 3 points of heuristics.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
QAOA for random k-SAT derives efficacy from an adiabatic manifold that supports rigorous performance guarantees at depth Θ(n²) and sublinear parameter optimization via SAMP at depth O(n).
citing papers explorer
-
Query-Efficient Quantum Approximate Optimization via Graph-Conditioned Trust Regions
A GNN predicts Gaussians over QAOA parameters to create graph-conditioned trust regions that reduce circuit evaluations for MaxCut from 85-343 down to 45 while keeping approximation ratios within 3 points of heuristics.
-
Mechanism of Efficacy in QAOA for Random k-SAT: From Adiabatic Manifold to Sublinear Parameter Optimization
QAOA for random k-SAT derives efficacy from an adiabatic manifold that supports rigorous performance guarantees at depth Θ(n²) and sublinear parameter optimization via SAMP at depth O(n).