QAOA is analyzed with QFI on cyclic and complete graphs showing entanglement redistributes sensitivity from diagonal to off-diagonal terms, and a QFI-informed mutation heuristic improves mean energy and reduces variance on 7- and 10-qubit instances.
Quantum approximate optimization of non-planar graph problems on a planar superconducting pr ocessor,
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Exploring Entanglement and Parameter Sensitivity in QAOA through Quantum Fisher Information
QAOA is analyzed with QFI on cyclic and complete graphs showing entanglement redistributes sensitivity from diagonal to off-diagonal terms, and a QFI-informed mutation heuristic improves mean energy and reduces variance on 7- and 10-qubit instances.