Application benchmark for quantum optimization on electromobility use case

Battery electric service vehicles are one step to reduce CO2 emissions in the mobility sector. We present an use case for optimal charging scheduling in combination with local solar power generation to minimize power grid usage. The study compares results obtained with a classical optimizer and with a quantum computing algorithm on real quantum hardware. It is shown that for most benchmark experiments, the quantum computing method yields an optimal solution however the quantum approximate optimization algorithm is more sensitive to penalty factors than the classical optimization. Additionally, we present a comparison of the computing times and give a brief review of the current state of IBM's gate-based quantum computing.