  Optimization of pre-production vehicle configurations is one of the
challenges in the automotive industry. Given a list of tests requiring cars
with certain features, it is desirable to find the minimum number of cars that
cover the tests and obey the configuration rules. In this paper, we model the
problem in the framework of satisfiability and solve it by utilizing the newly
introduced hybrid constrained quadratic model (CQM) solver provided by D-Wave.
The problem definition is based on the "Optimizing the Production of Test
Vehicles" use case given in the BMW Quantum Computing Challenge. We formulate a
constrained quadratic model for the problem and use a greedy algorithm to
configure the cars. We benchmark the results obtained from the CQM solver with
the results from the classical solvers like CBC (Coin-or branch and cut) and
Gurobi. We conclude that the performance of the CQM solver is comparable to
classical solvers in optimizing the number of test vehicles. As an extension to
the problem, we describe how the scheduling of the tests can be incorporated
into the model.
