industry
Transportation & Mobility
Target traffic flow models, evaluate vehicle routing, and explore optimization in autonomous systems. Hybrid quantum-classical solutions are designed to map complex optimization problems, addressing classical bottlenecks in global transportation infrastructure.
BMW Group
BMW Group partnered with Pasqal to explore the integration of quantum computing workflows within its manufacturing and development R&D units.
Scale Computational Workloads with Hybrid Infrastructure
Quantum for Transportation & Mobility
How can quantum computing optimize transportation systems?
Hybrid quantum architectures are being engineered to evaluate real-time traffic flow parameters, route planning for autonomous systems, and logistics network design. The technology aims to model the dynamic complexity of modern multi-modal transportation networks.
What mobility challenges can Pasqal’s quantum computers address?
Key applications include vehicle routing optimization, traffic signal coordination, electric vehicle charging infrastructure planning, ride-sharing fleet optimization, delivery route planning, airport operations scheduling, railway timetabling, and maritime logistics optimization.
How does quantum computing support electric vehicle adoption?
Quantum solvers are being evaluated to model EV charging station placement, simulate grid load from charging demand, and evaluate smart charging schedules. Additionally, neutral-atom QPUs target battery technology improvements through advanced materials simulation.
Can quantum computing reduce transportation emissions?
Yes, by optimizing routes to minimize fuel consumption, coordinating traffic lights to reduce idling, improving logistics efficiency to reduce empty miles, designing lighter materials for vehicles, optimizing public transit to increase ridership, and planning sustainable urban mobility infrastructure.
How does quantum computing improve autonomous vehicle systems?
Quantum architectures offer potential pathways to model real-time route planning for AV fleets, simulate complex traffic scenarios, and evaluate multi-vehicle interactions. Researchers are utilizing hybrid solvers to address complex decision-making parameters that AVs face in dynamic urban environments.