Quantum computing use cases in automotive

Where quantum computing could apply in automotive

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.

Manufacturing scheduling: one reported deployment and one benchmark

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.

Battery materials: algorithm studies, estimates and announcements

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 trial wavefunctions are needed. In November 2022 Bosch and IBM announced a strategic engagement on materials for fuel cells, batteries, electric engines and sensors. The release describes mostly plans and calls early results promising without quantifying them.

For scale, Google Quantum AI’s October 2023 post estimates tens of millions of physical qubits for four candidate structures of the cathode material LiNiO2, studied with BASF, and says that is out of reach of the first error-corrected computers. The energy guide covers the same estimate.

Design and engineering simulation: simulators and a solver subroutine

On June 1, 2026 Nissan and Quemix announced a hybrid quantum-classical algorithm for vehicle aerodynamics. It ran on a quantum computer simulator and reproduced fluid behavior with accuracy comparable to conventional methods. The release gives no error metrics or qubit count. The baseline is a lattice Boltzmann method that takes long to compute, and the release says the results suggest potential to cut computation from about a day to minutes. That is a hedged projection, not a result.

In June 2024 Classiq, NVIDIA and the BMW Group reported a quantum program for choosing and connecting electrical and mechanical components, such as motors, batteries and coolers, in a vehicle. The circuit was simulated on GPUs, no hardware was used, and the release gives no quantitative results and says more effort is needed before the solution is industrially applicable. BMW’s own February 2025 article says the technology is not yet used for industry-relevant problems.

A preprint by IonQ and Synopsys authors from March 2026, revised in August, places a quantum solver for a graph partitioning problem inside the LS-DYNA engineering simulation software. The quantum step reduces fill-in when solving large sparse linear systems. Tested on a sedan model, a jet engine model, a drill and an impeller, with meshes up to 35 million elements, the abstract reports amortized wall-clock time improved by up to 14.6 percent in specific cases and by at least 5.9 percent across all models. Simulations ran up to 150 qubits, with hardware runs on trapped-ion hardware. The abstract does not define the baseline, and the quantum step is one subroutine inside a classical solver.

Autonomy and supply chain: challenge entries

In December 2024 BMW Group and Airbus announced the winners of a quantum computing challenge hosted by TQI, with more than 100 submissions. Quandela, a French start-up, won the machine learning category with work on generating critical driving scenarios, such as turning limited daytime data into realistic night conditions, and 4colors Research won the logistics category on supply chain emissions and cost. The release describes the work as still in the early stages and does not say whether any result came from quantum hardware. Toyota Systems and Fujitsu tested a quantum-inspired system on parts distribution in 2020, which the logistics guide describes.

Quantum sensing for navigation is a separate topic from computing. See the series index for the transport and defense guides.

What the value estimates say, and what they do not

The McKinsey Quantum Technology Monitor, April 2026, lists automotive under advanced industries, with aerospace and defense, advanced electronics and semiconductors. It rates automotive “low” for 2025 to 2030 and “medium” for 2030 to 2035 and gives no dollar figure for it, so TQI does not quote one. The report says its value estimates are approximate and not definitive projections of business value.

The report also contains a readiness chart built on TQI’s Quantum Index 2025, which scores the 15 most quantum-ready companies in each industry. For automotive, European companies total 318 points and rank first, ahead of Asia at 229 and North America at 113. McKinsey’s note beside the chart, which covers Europe’s lead in most industries, says almost half of the top three companies are German.

How to read a quantum automotive claim

1. Who says it? The Ford Otosan figure comes from D-Wave’s release, and the Nissan and Synopsys results from company announcements and preprints. The paint shop paper was accepted at an IEEE conference.
2. What is the baseline? Neither the Ford Otosan release nor the Synopsys preprint’s abstract defines it fully.
3. Hardware, simulator or estimate? Nissan, BMW’s mechatronic work and the Ford study used simulators, and the Volkswagen, Xanadu and Mercedes-Benz studies are estimates or algorithm studies.
4. How big is the quantum part? In the Ford Otosan hybrid solver and in LS-DYNA, the quantum step is one component of a classical workflow.
5. Best case or all cases? The 14.6 percent figure is a best case, and 5.9 percent holds across all models tested.
6. Result or announcement? The Bosch and IBM release and the challenge winners report plans and early work, and BMW says the technology is not yet used for industry-relevant problems.

What a carmaker or supplier can do now

• Pick the scheduling and sequencing problems where today’s solver takes too long and measure the classical baseline. Ford Otosan’s case started from a problem with more than 1,500 variants.
• For materials, track the resource estimates, which say what hardware must exist before a battery simulation matters.
• Ask any vendor or adviser for the baseline, the problem size, and whether the run used hardware, a simulator or an estimate.
• For company, funding and patent data across quantum, see TQI Data.

Related guides

The series index lists every industry in this series. Battery and cathode estimates also appear in the energy guide, routing and supply chain in the logistics guide, and the finance guide has the longest list of bank pilots.

Questions

Sources

1. McKinsey Quantum Technology Monitor 2026, April 2026 (automotive rating and readiness chart; the readiness chart cites TQI’s Quantum Index 2025).
2. Quantum computing at the BMW Group, BMW Group, February 4, 2025.
3. In production: Ford Otosan deploys vehicle manufacturing application built with D-Wave technology, D-Wave press release, March 31, 2025.
4. Multi-car paint shop optimization with quantum annealing, Yarkoni et al., arXiv:2109.07876, September 16, 2021 (accepted at IEEE QCE21).
5. Volkswagen and IQM Quantum Computers release study on battery simulation, IQM press release, November 15, 2024.
6. Contextual subspace auxiliary-field quantum Monte Carlo: improved bias with reduced quantum resources, Kiser, Beuerle and Simkovic, arXiv:2408.06160, August 12, 2024 (preprint).
7. Volkswagen AG und Xanadu starten Programm zur Simulation von Batteriematerialien mit Quantenalgorithmen, Volkswagen Group press release (German), October 18, 2022.
8. Fault-tolerant resource estimate for quantum chemical simulations: case study on Li-ion battery electrolyte molecules, Kim et al., Physical Review Research 4, 023019, April 7, 2022.
9. Towards the simulation of transition-metal oxides of the cathode battery materials using VQE methods, Farag and Ghosh, arXiv:2208.07977, August 16, 2022 (preprint).
10. Bosch partnering with IBM on strategic quantum computing materials science engagement, IBM Newsroom, November 9, 2022.
11. Developing industrial use cases for physical simulation on future error-corrected quantum computers, Google Research blog, October 12, 2023.
12. Nissan and Quemix successfully demonstrate world’s first vehicle aerodynamic simulation using quantum algorithm, Nissan, June 1, 2026.
13. Classiq collaborates with BMW Group and NVIDIA to drive quantum computing applicability in electrical systems engineering, Classiq press release, June 20, 2024.
14. End to end performance of quantum-accelerated large-scale linear algebra workflows, Zhu et al., arXiv:2603.15515, submitted March 16, 2026 (v4 August 25, 2026; preprint).
15. BMW Group and Airbus reveal winners of quantum computing challenge, BMW Group press release, December 10, 2024.
16. Fujitsu and Toyota Systems optimize large-scale supply chain logistics, Fujitsu press release, September 10, 2020.