  Quantum annealing algorithms belong to the class of meta-heuristic tools,
applicable for solving binary optimization problems. Hardware implementations
of quantum annealing, such as the quantum processing units (QPUs) produced by
D-Wave Systems, have been subject to multiple analyses in research, with the
aim of characterizing the technology's usefulness for optimization and sampling
tasks. In this paper, we present a real-world application that uses quantum
technologies. Specifically, we show how to map certain parts of the real-world
traffic flow optimization problem to be suitable for quantum annealing. We show
that time-critical optimization tasks, such as continuous redistribution of
position data for cars in dense road networks, are suitable candidates for
quantum applications. Due to the limited size and connectivity of
current-generation D-Wave QPUs, we use a hybrid quantum and classical approach
to solve the traffic flow problem.
