Mind the Gaps: Quantum Optimization for Efficient Electric and Autonomous Freight Dispatch with IonQ and Einride The shift to electric freight is accelerating, and its economics hinge on a planning problem that conventional routing software was not designed to handle. Research conducted by Einride, a technology company building the infrastructure for electric and autonomous freight, alongside Fraunhofer and Rewe, found that optimizing electric fleet operations from the ground up reduced fleet-level total cost of ownership by 8–13%, compared to roughly 3% for straightforward 1:1 replacement of diesel trucks with EVs. Electric fleets introduce charging schedules, energy constraints, and route interdependencies that make planning substantially more complex than conventional trucking. That complexity, managed well, is where the economic advantage lives. That optimization advantage depends entirely on how well the plan holds, and how well the system recovers when it doesn't. Einride operates its fleet through Saga, an AI-powered platform that connects vehicles, infrastructure, and data to manage freight operations at scale. Shipment cancellations are a daily feature of large-scale logistics, leaving idle gaps in pre-optimized vehicle schedules that directly erode the fleet utilization rates that make electric freight economically viable. Einride's fleet operations team deals with them continuously, using its Electric Vehicle Routing Problem (E-VRP) solver to slot in replacement shipments from a waiting pool. Each gap carries hard constraints: vehicle charging limits, driving time regulations, time windows, and driver shift boundaries. The solver is fast and effective for individual gaps. What it does not naturally account for is the interaction between gaps on different vehicles: how two replacement shipments assigned to nearby routes might conflict operationally, create scheduling dependencies downstream, or compound risk across the fleet when executed together. That interaction between concurrent assignments is where classical gap-filling solvers are structurally limited, and where the quantum formulation is specifically designed