  In this paper, we propose a quantum computing oriented benchmark for
combinatorial optimization. This benchmark, coined as QOPTLib, is composed of
40 instances equally distributed over four well-known problems: Traveling
Salesman Problem, Vehicle Routing Problem, one-dimensional Bin Packing Problem
and the Maximum Cut Problem. The sizes of the instances in QOPTLib not only
correspond to computationally addressable sizes, but also to the maximum length
approachable with non-zero likelihood of getting a good result. In this regard,
it is important to highlight that hybrid approaches are also taken into
consideration. Thus, this benchmark constitutes the first effort to provide
users a general-purpose dataset. Also in this paper, we introduce a first full
solving of QOPTLib using two solvers based on quantum annealing. Our main
intention with this is to establish a preliminary baseline, hoping to inspire
other researchers to beat these outcomes with newly proposed quantum-based
algorithms.
