Constraint-aware initialization and hybrid XY-X mixer in QAOA for VRP yield lower average energies and higher feasible-solution ratios than standard QAOA across ideal, finite-shot, and noisy simulations.
arXiv preprint arXiv:2512.10813
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Improving Feasibility in Quantum Approximate Optimization Algorithm for Vehicle Routing via Constraint-Aware Initialization and Hybrid XY-X Mixing
Constraint-aware initialization and hybrid XY-X mixer in QAOA for VRP yield lower average energies and higher feasible-solution ratios than standard QAOA across ideal, finite-shot, and noisy simulations.