Further QAOA considerations
In the previous chapter, the QAOA implementation moved through a range of parameter values, including param1 and param2. We found that, in the parameter landscape, varying costs were returned and certain pairs of parameters sometimes yielded the lowest cost. Can we use an optimizer to move toward the lowest cost more efficiently than mapping the whole parameter landscape? This will be the first area we will investigate. In addition, in the previous chapter, we stated that by repeatedly applying a combination of Z and X rotations with appropriate parameters, the probability profile can be modified more effectively so that we see the minimum cost. We will review this as well.
Full QAOA hybrid algorithm using a classical parameter optimizer
Classical optimization algorithms have various ways of evaluating the landscape for a lower cost value and continuing to move towards a minima. However, we know that sometimes these algorithms can get stuck in a local...
