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Can the problem of minimizing a single-variable degree 2 polynomial over a bounded interval be recast as finding the ground state of an Ising model (up to discretization of the continuous variable)?
Think of the portfolio optimization example.
Suppose you managed to find a heuristic for the MaxCut problem that outperforms Goemans-Williamson on a 100-vertices problem. Besides, out of theoretical arguments (maybe predictions on the size of the maximum cut for a typical graph like yours), you have sound reasons to believe that it has found a close to optimal solution. What do you conclude?
Which type of classical algorithms quantum optimization algorithms like the Quantum Approximate Optimization Algorithm most resemble to?