  Quantum annealing has great promise in leveraging quantum mechanics to solve
combinatorial optimisation problems. However, to realize this promise to it's
fullest extent we must appropriately leverage the underlying physics. In this
spirit, I examine how the well known tendency of quantum annealers to seek
solutions where more quantum fluctuations are allowed can be used to trade off
optimality of the solution to a synthetic problem for the ability to have a
more flexible solution, where some variables can be changed at little or no
cost. I demonstrate this tradeoff experimentally using the reverse annealing
feature a D-Wave Systems QPU for both problems composed of all binary
variables, and those containing some higher-than-binary discrete variables. I
further demonstrate how local controls on the qubits can be used to control the
levels of fluctuations and guide the search. I discuss places where leveraging
this tradeoff could be practically important, namely in hybrid algorithms where
some penalties cannot be directly implemented on the annealer and provide some
proof-of-concept evidence of how these algorithms could work.
