Quantum computing use cases in automotive fall into three groups: battery and materials simulation, design and engineering simulation, and manufacturing optimization. As of Q4 2026 the one reported deployment is a hybrid scheduling application at Ford Otosan, according to its vendor, and the other examples are simulations, preprints and resource estimates.
The McKinsey Quantum Technology Monitor, April 2026, rates the economic value of quantum computing in automotive as low for 2025 to 2030 and medium for 2030 to 2035, and gives no separate dollar range for the industry. The BMW Group names three application areas: materials science, engineering such as aerodynamics and crash simulation, and processes such as logistics and production optimization.
The published carmaker examples fall into the same areas.
Ford Otosan sequences Ford Transit vans, which come in more than 1,500 variants, under body shop, paint shop and assembly line constraints. In March 2025 D-Wave announced that Ford Otosan had deployed a scheduling application built on its hybrid solvers, available through the Leap cloud service, and that scheduling time for 1,000 vehicles per run fell from 30 minutes to less than five.
Ford Otosan plans to extend it to more body shops, paint shops, assembly lines and buffer zones.
A 2021 paper accepted at the IEEE Quantum Computing and Engineering conference by researchers including Volkswagen authors tested a multi-car paint shop problem, which minimizes color switches in a paint queue, using test instances built from real data from a Wolfsburg factory. Two annealers and a hybrid solver were compared with classical solvers.
Pure quantum processors did well on smaller problems and the hybrid algorithm on intermediate ones, and for large problems every method approached the performance of a simple greedy algorithm.
Volkswagen and IQM released a study in November 2024, based on a preprint, on a hybrid method for simulating the nitrogen dimer and the reductive decomposition of ethylene carbonate, a solvent in lithium-based batteries. The authors report that it outperforms several established ground-state energy algorithms and reaches chemical accuracy with less than half the original number of qubits.
IQM says it simulated the reaction with 13 qubits in a contextual subspace drawn from a 32-qubit active space. Neither source says whether the quantum part ran on hardware.
Volkswagen and Xanadu started a multi-year program in October 2022 on algorithms for simulating battery materials, aimed at Xanadu’s future fault-tolerant machines. Its first report, published in Physical Review A in September 2022, estimates the resources needed to simulate the cathode material dilithium iron silicate. It is a resource estimate and not a completed simulation.
Mercedes-Benz R&D North America and PsiQuantum authors published a resource estimate in Physical Review Research in April 2022 for simulating Li-ion electrolyte molecules on a fault-tolerant photonic computer, and propose methods that could cut runtime by up to an order of magnitude. It reports no experimental run.
Ford’s research group posted a 2022 preprint that uses the variational quantum eigensolver to estimate the ground-state energy of LiCoO2, a cathode material, in models of its charged and discharged states. All runs were on a statevector simulator. The results agree quantitatively with the classical CCSD method and less well with CASCI, which the authors take as a sign that better…
