  Quantum annealing solves combinatorial optimization problems by finding the
energetic ground states of an embedded Hamiltonian. However, quantum annealing
dynamics under the embedded Hamiltonian may violate the principles of adiabatic
evolution and generate excitations that correspond to errors in the computed
solution. Here we empirically benchmark the probability of chain breaks and
identify sweet spots for solving a suite of embedded Hamiltonians. We further
correlate the physical location of chain breaks in the quantum annealing
hardware with the underlying embedding technique and use these localized rates
in a tailored post-processing strategies. Our results demonstrate how to use
characterization of the quantum annealing hardware to tune the embedded
Hamiltonian and remove computational errors.
