Our team was recently selected as the winner in the Quantum-Powered Logistics category of the Airbus and BMW Group Quantum Computing Challenge. This prestigious challenge focused on leveraging quantum technologies to address complex industrial problems in the automotive and aviation sectors. It was organized by Airbus and BMW, with support from Amazon Web Services (AWS) and The Quantum Insider.
The competition attracted over 420 teams and received more than 100 detailed proposals from across the globe. The challenge featured five tracks, each targeting a unique industrial problem, with our team focusing on optimizing production planning and logistics for companies like Airbus and BMW using quantum computing.
The event brought together researchers and startups eager to develop quantum solutions for real-world industrial challenges. After months of intense competition, the winners were announced in December 2024 at Q2B 2024 Silicon Valley, a leading quantum computing conference held in Santa Clara, California. The award ceremony took place at the Computer History Museum in Mountain View, alongside discussions on the future of quantum technologies.
The challenge unfolded over two main phases spanning about a year. During Phase I, participants were tasked with developing optimization solutions for a simplified supply chain problem. Our team realized that the simplified problem could be effectively solved using classical computing, so we designed efficient classical algorithms that could run on commodity hardware and deliver solutions in minutes. Specifically, we optimized the supply chain for the Airbus A380, which involves the allocation of over 4 million parts across 50 assembly sites worldwide. Our algorithm was able to compute the optimal allocation in under 30 minutes on a standard laptop.
At the end of Phase I in April 2024, three finalists from each track were selected to move to Phase II, where the problem became more complex and closely aligned with real-world scenarios. Finalists had the opportunity to collaborate with experts from Airbus and BMW, who provided valuable insights into their operations and business cases, further refining our solution.
The core problem posed by Airbus and BMW was to optimize the assignment of parts to assembly sites to minimize costs—both direct (production and transportation) and indirect (such as CO2 emissions). The task was complicated by the fact that products like airplanes and cars are assembled from a large number of component parts. Each component part may be sourced from multiple global locations, and each assembly site receives parts from others, assembles them, and ships the finished components to the next site.
The cost of assembling and transporting these parts varies depending on factors such as labor costs and the chosen transportation method—air, sea, or land. Moreover, each decision influences the overall efficiency of the supply chain, including costs, emissions, and lead times. The key challenge was making multiple, interdependent decisions that lead to the optimal global supply chain configuration, balancing cost, time, and environmental impact.
To solve this problem, we turned to mathematical optimization, a field dedicated to solving complex problems like this one. When dealing with large supply chains, such as those of Airbus and BMW, human planners are prone to making suboptimal decisions due to the sheer complexity. For instance, if there are 60 component parts, and each needs to be assigned to one of 60 assembly sites, there are more possible configurations than the number of atoms in the universe, making the task difficult for humans and challenging for computers.
While traditional computers can process a large number of possibilities, the sheer scale of the problem requires sophisticated optimization algorithms to generate the best solution. Problems of this scale require advanced optimization techniques to achieve significant cost savings and resource efficiency for businesses.
Quantum computing promises a radical improvement in solving such optimization problems due to its ability to process vast amounts of data simultaneously, thanks to the principles of quantum mechanics. However, programming quantum computers and building the necessary hardware remain complex challenges.
Our team, specializing in designing advanced optimization algorithms, opted for a hybrid approach. Recognizing that reliable quantum computers are still in the future, we began by implementing classical optimization techniques that could run today on classical hardware. However, we didn’t stop there—knowing that quantum computing holds the key to further optimization, we also introduced novel quantum counterparts of these algorithms to future-proof our solution.
We employed several advanced techniques, such as feasibility pump, local search, and path relinking, which are well-known optimization methods. While these terms might sound complex, they are effective strategies for improving solutions to optimization problems. By translating these strategies into quantum versions, we can take advantage of future quantum capabilities when they become available at scale. We demonstrated, using today's quantum computers with limited capabilities, that the approach is viable and will scale well on larger and more reliable quantum machines.
Judges praised our end-to-end solution, which combined advanced optimization techniques and quantum implementations. Our solution not only delivers results on classical systems today but is also prepared to leverage the power of quantum computing when it becomes more accessible. Our solution was designed to be flexible, allowing us to switch between classical and quantum implementations as needed. This approach ensures that our solution is not only effective today but also ready for future advancements in quantum computing.
We are proud to see our innovative approach to production planning and logistics optimization recognized by industry leaders, and we’re excited about the potential for quantum technologies to transform industries like aviation and automotive manufacturing.
We believe that our work not only demonstrates the power of advanced mathematical optimization and quantum computing but also shows the incredible potential for businesses to reduce costs, save resources, and optimize their operations. As quantum computing advances, we are eager to continue exploring ways to harness its capabilities to solve some of the world’s most complex industrial challenges.
Our team is committed to pushing the boundaries of optimization and algorithm design, both classical and quantum, and seeing our solutions drive efficiency and sustainability across industries.
You can also read our press release about our participation in the challenge.