Approximate quantum annealing supplies effective warm-start parameters for QAOA while EHQO guides optimization through intermediate Hamiltonians, yielding better performance than random initialization on hard 2-SAT problems in numerical tests.
De Raedt, Computer Physics Reports7, 1 (1987)
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Classical simulation of quantum annealing for the 1D Hubbard model up to 40 qubits reports substantial speed-up over Bethe-ansatz methods for half-filled cases.
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Quantum annealing inspired algorithms for the NISQ Era
Approximate quantum annealing supplies effective warm-start parameters for QAOA while EHQO guides optimization through intermediate Hamiltonians, yielding better performance than random initialization on hard 2-SAT problems in numerical tests.