  I describe how real quantum annealers may be used to perform local (in state
space) searches around specified states, rather than the global searches
traditionally implemented in the quantum annealing algorithm. Such protocols
will have numerous advantages over simple quantum annealing. By using such
searches the effect of problem mis-specification can be reduced, as only energy
differences between the searched states will be relevant. The quantum annealing
algorithm is an analogue of simulated annealing, a classical numerical
technique which has now been superseded. Hence, I explore two strategies to use
an annealer in a way which takes advantage of modern classical optimization
algorithms. Specifically, I show how sequential calls to quantum annealers can
be used to construct analogues of population annealing and parallel tempering
which use quantum searches as subroutines. The techniques given here can be
applied not only to optimization, but also to sampling. I examine the
feasibility of these protocols on real devices and note that implementing such
protocols should require minimal if any change to the current design of the
flux qubit-based annealers by D-Wave Systems Inc. I further provide
proof-of-principle numerical experiments based on quantum Monte Carlo that
demonstrate simple examples of the discussed techniques.
