The paper decomposes dynamical Lie algebras of XY-mixer topologies and demonstrates warm-starting QAOA via pre-training on restricted generators to improve convergence on constrained optimization problems.
Hodson, Brendan Ruck, Hugh Ong, David Garvin, and Stefan Dulman
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Coupling-Grouped XY-QAOA enables joint anomaly-feature selection via a constraint-preserving grouped-angle QAOA variant, achieving 45.9-61.3% circuit depth reduction and larger feasible executions (64 qubits at p=2) on IBM Heron hardware compared to standard approaches.
First experimental run of CV-QAOA on a programmable quad-rail lattice cluster state shows depth-1 to depth-2 improvement but limited further gains due to noise, unlike ideal simulations.
Benchmarking on IBM heavy-hex processors shows WS-QAOA incurs catastrophic decoherence from nonlocal gates while HE-VQNN preserves coherence but lacks expressibility for CVaR tail correlations, exposing NISQ connectivity limits for dense financial optimization.
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The Lie Algebra of XY-mixer Topologies and Warm Starting QAOA for Constrained Optimization
The paper decomposes dynamical Lie algebras of XY-mixer topologies and demonstrates warm-starting QAOA via pre-training on restricted generators to improve convergence on constrained optimization problems.