QUANTUM ANNEALING FOR MOBILITY STUDIES: GO/NO-GO MAPS VIA QUANTUM-ASSISTED MACHINE LEARNING

We present the results of an exploratory investigation of applying a hybrid quantum-classical architecture to an off-road vehicle mobility problem, namely the generation of go/no-go maps posed as a machine learning problem.

The premise of this work rests on two observations. First, quantum computing allows in principle for algorithms that provide a speedup over the best known classical counterparts. However, as it is to be expected of such novel and complex tools (both hardware and algorithmic) at this early developmental stage, current quantum algorithms do not always perform well on real-world problems. Second, complex physics-based vehicle and terramechanics models and simulations, currently advocated for high-fidelity high-accuracy ground vehicle–terrain interaction analyses, pose significant computational burden, especially when applied to mobility studies which may require numerous simulation runs.

We describe the Quantum-Assisted Helmholtz Machine formulation, suitable to be implemented on a quantum annealer such as the D-Wave 2000Q machine, discuss the high-performance classical computing framework used to generate through simulation the training and test sets, and provide the results of our investigations and analysis into the performance of the machine learning model and its predictive capabilities for generating go/no-go mobility maps.

This work represents a contribution to an ongoing effort of exploring the applicability of the emerging field of quantum computing to challenging engineering and scientific problems.