  A quantum annealing solver for the renowned job-shop scheduling problem (JSP)
is presented in detail. After formulating the problem as a time-indexed
quadratic unconstrained binary optimization problem, several pre-processing and
graph embedding strategies are employed to compile optimally parametrized
families of the JSP for scheduling instances of up to six jobs and six machines
on the D-Wave Systems Vesuvius processor. Problem simplifications and
partitioning algorithms, including variable pruning and running strategies that
consider tailored binary searches, are discussed and the results from the
processor are compared against state-of-the-art global-optimum solvers.
