A fixed set of four linear QAOA angle coefficients trained on one random Ising instance transfers to other instances with only a small loss in approximation ratio, eliminating per-instance optimization.
Quantum Approximate Optimization Algorithm: Performance, Mechanism, and Implemen- tation on Near-Term Devices,
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Transferring linearly fixed QAOA angles: performance and real device results
A fixed set of four linear QAOA angle coefficients trained on one random Ising instance transfers to other instances with only a small loss in approximation ratio, eliminating per-instance optimization.