D-Wave Quantum Inc. has announced two distinct quantum computing application milestones: the deployment of a hybrid quantum-classical production scheduling system at Ford Otosan, and the completion of a quantum-assisted drug discovery proof-of-concept in collaboration with Japan Tobacco (JT). Both projects utilize D-Wave’s annealing quantum computing technology and demonstrate applied use cases in industrial and scientific contexts.
At Ford Otosan, a joint venture between Ford Motor Company and Koç Holding, D-Wave’s quantum hybrid solver has been integrated into vehicle production sequencing for the Ford Transit line. With more than 1,500 vehicle variants produced on the same line, sequencing the order of production is a high-dimensional optimization task subject to constraints from the body shop, paint shop, and assembly processes. Ford Otosan used D-Wave’s Leap™ quantum cloud service to minimize production disruptions by optimizing vehicle sequencing in a manner that outperformed both proprietary and open-source classical solvers. The hybrid-quantum approach reduced scheduling times for 1,000-vehicle runs from 30 minutes to under five, compared to 10 minutes with the in-house classical solver and over one hour with open-source methods. The deployed application is currently operational and being extended to other manufacturing stages.
Separately, D-Wave and the pharmaceutical division of Japan Tobacco completed a quantum-hybrid proof-of-concept aimed at accelerating the generative design of small-molecule drug candidates. The collaboration integrated D-Wave’s annealing quantum processor into the training loop of large language models (LLMs), such as transformer-based generative architectures. The joint team evaluated whether quantum sampling could improve the generation of chemically valid and drug-like molecules. The QPU-assisted LLM training produced molecules with higher quantitative estimates of drug-likeness and a broader range of valid structures compared to baseline classical training alone. The annealing quantum computer was used to generate lower-energy samples, which were then incorporated into the model’s learning process. JT reports that this is the first time annealing quantum computing has been shown to outperform classical methods in generative model training for molecular discovery.
Both initiatives reflect ongoing efforts to integrate quantum computing into domain-specific applications. In the case of Ford Otosan, the focus is on constrained industrial optimization with real-time implications for production efficiency and throughput. For JT, the emphasis is on molecular structure generation under property constraints, contributing to the pre-clinical phase of drug discovery pipelines. In both projects, D-Wave’s annealing platform was used not in isolation but as part of hybrid quantum-classical workflows, highlighting the importance of interoperability and algorithmic co-design for near-term quantum advantage in applied settings.
Ford Otosan plans to expand quantum optimization to other sections of its manufacturing process, while JT is exploring further use of quantum-enhanced generative models for medicinal chemistry applications. Both collaborations involved support from D-Wave’s professional services teams and leveraged its cloud-based infrastructure to conduct scaled quantum experiments.
Full technical details of the Japan Tobacco and Ford Otosan announcements are available in D-Wave’s official press releases here and here.
March 31, 2025
