Optimizing the Production of Test Vehicles Using Hybrid Constrained Quantum Annealing

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