  The Quantum Alternating Operator Ansatz is a generalization of the Quantum
Approximate Optimization Algorithm (QAOA) designed for finding approximate
solutions to combinatorial optimization problems with hard constraints. In this
paper, we study Maximum $k$-Vertex Cover under this ansatz due to its modest
complexity, while still being more complex than the well studied problems of
Max-Cut and Max E3-LIN2.
  Our approach includes (i) a performance comparison between easy-to-prepare
classical states and Dicke states as starting states, (ii) a performance
comparison between two $XY$-Hamiltonian mixing operators: the ring mixer and
the complete graph mixer, (iii) an analysis of the distribution of solutions
via Monte Carlo sampling, and (iv) the exploration of efficient angle selection
strategies.
  Our results are: (i) Dicke states improve performance compared to
easy-to-prepare classical states, (ii) an upper bound on the simulation of the
complete graph mixer, (iii) the complete graph mixer improves performance
relative to the ring mixer, (iv) numerical results indicating the standard
deviation of the distribution of solutions decreases exponentially in $p$ (the
number of rounds in the algorithm), requiring an exponential number of random
samples to find a better solution in the next round, and (v) a correlation of
angle parameters which exhibit high quality solutions that behave similarly to
a discretized version of the Quantum Adiabatic Algorithm.
