The automotive industry will be one of the primary value pools for QC, with a high impact noticeable by about 2025. Most of the early value added will come from solving complex optimization problems, including processing vast amounts of data to accelerate learning in autonomous-vehicle-navigation algorithms. In later years, QC has the potential to have a positive effect on many areas in the automotive industry, such as vehicle routing and route optimization, material and process research, and the security of connected driving. Moreover, QC can also provide a boost to automotive players transitioning into the electric-vehicle (EV) era by notably accelerating R&D of novel technologies. For instance, companies can speed the transition of their more traditional technology spectrum towards more relevant technologies such as cooling of EV batteries. Likewise, the simulation of material process research for batteries and fuel cells could be a field where QC could be deployed with a chance of success.
Near-term opportunities for QC – for the time frame through 2025 – are expected to surface in product development and R&D. Relevant use cases will primarily relate to solving simple optimization problems or involve parallel data processing for simple quantum artificial-intelligence/machine- learning (AI/ML) algorithms. These QC applications will be executed as part of a hybrid solution, where bits of a larger problem, processed by a High-Performance Computer (HPC), are outsourced to a quantum computer and results are fed back into the HPC flow. Possible optimization use cases include the combinatorial optimization of multichannel logistics, highly local traffic-flow optimization, and improvements in vehicle routing. Quantum AI/ML might involve the time-efficient training of autonomous-driving algorithms due to an increase in the parallel processing of large amounts of data.
In the midterm – that is, the timeframe from 2025 through 2030 – the authors expect the QC activities in the automotive industry will focus on things like simulations (heat and mass transfer, fluid dynamics as well as material properties at the atomic level – relevant for the development of battery and fuel cell materials). In addition, more complex city traffic simulations could become possible as well as solving large-scale multimodal fleet routing problems. Plus, the capability of more advanced quantum computers to process large amounts of data will help engineers and developers to implement solutions for enhanced pattern recognition.