  We propose GM-QAOA, a variation of the Quantum Alternating Operator Ansatz
(QAOA) that uses Grover-like selective phase shift mixing operators. GM-QAOA
works on any NP optimization problem for which it is possible to efficiently
prepare an equal superposition of all feasible solutions; it is designed to
perform particularly well for constraint optimization problems, where not all
possible variable assignments are feasible solutions. GM-QAOA has the following
features: (i) It is not susceptible to Hamiltonian Simulation error (such as
Trotterization errors) as its operators can be implemented exactly using
standard gate sets and (ii) Solutions with the same objective value are always
sampled with the same amplitude.
  We illustrate the potential of GM-QAOA on several optimization problem
classes: for permutation-based optimization problems such as the Traveling
Salesperson Problem, we present an efficient algorithm to prepare a
superposition of all possible permutations of $n$ numbers, defined on $O(n^2)$
qubits; for the hard constraint $k$-Vertex-Cover problem, and for an
application to Discrete Portfolio Rebalancing, we show that GM-QAOA outperforms
existing QAOA approaches.
