The future of quantum computing holds immense potential in sectors like automotive and aerospace, where advanced simulations are critical for innovation. In partnership with Airbus and BMW Group, the Quantum Mobility Quest presents an opportunity for quantum solutions to solve highly complex problems.
The quest focuses on finding quantum-native applications that can revolutionize structural analysis, heat simulation, and materials design in transportation. Three teams, CogniFrame, TU Delft and Lightsolver, are pushing the boundaries with their unique quantum-based approaches, aiming to redefine how industries approach these challenges.
CogniFrame: Quantum Solutions for Structural Simulations
CogniFrame, a quantum and hybrid quantum algorithm solutions provider, entered the Quantum Mobility Quest with a novel quantum approach to structural simulations. CEO Vish Ramakrishnan described how their 2023 solution addresses the challenge of simulating large-scale structures—an area where traditional computational methods face scalability limits. “We chose to enter the challenge as a ‘Golden App’ as part of the Reverse Track,” Ramakrishnan said. “We picked a problem that we felt would resonate with Airbus and BMW, offering a compelling quantum solution that would address the scalability challenges of conventional computing while being time- and cost-effective.”
CogniFrame’s Finite Element Analysis (FEA) solution uses a Quantum Hamiltonian simulation approach. By reducing structural simulations to a Hamiltonian problem, the team overcame limitations inherent to classical methods like the Harrow-Hassidim-Lloyd (HHL) algorithm. This method has wide-ranging applicability across industries that rely on structural dynamics, including aerospace, automotive, and even spacecraft design.
Ramakrishnan emphasized the potential impact of their solution on transportation. “Our solution focuses on analyzing the structural dynamics of a system, which is critical in areas like impact analysis and structural integrity. Quantum advantages, such as improvements in time and scale, could make it possible to deliver simulations far beyond the capabilities of classical computing, leading to more efficient and safer designs.”
Despite these advancements, CogniFrame recognizes the challenges they faced during the competition. For Phase 1, the team concentrated on validating the feasibility and advantages of their solution, using open-source data to benchmark results. Now, their immediate goal is to further refine the solution for specific automotive and aviation use cases, while seeking industrial collaborations for commercialization.
Looking to the next decade, Ramakrishnan predicts that quantum computers will become robust enough to speed up complex simulations, particularly in areas where structural integrity and safety are vital. “Quantum computing has the potential to outperform classical methods in these areas, driving advancements in both the aviation and automotive sectors.”
TU Delft: Quantum Solutions for Composite Material Design
The Quantum Artificial Intelligence and Materials Science (QAIMS) lab at TU Delft took on the Quantum Mobility Quest with a focus on sustainable materials design. Their team explored quantum computational methods for optimizing the stacking sequence in laminated composite materials—a critical step in reducing the weight of designs like airplanes or wind turbine blades.
Laminated materials consist of multiple layers, and finding the optimal configuration for stiffness and weight reduction is a combinatorial problem with exponential scaling. This is where quantum methods come into play. “Stacking sequence retrieval is an area where quantum computational methods show significant potential,” said the TU Delft team. “Our goal was to investigate how quantum algorithms, like the Quantum Approximate Optimization Algorithm (QAOA), could improve these configurations for more sustainable designs.”
Their approach also involved examining classical tensor network methods, such as the Density Matrix Renormalization Group (DMRG), which can solve larger problems unattainable by quantum hardware alone. This hybrid approach allowed the team to gain preliminary insights into the performance of quantum algorithms for this type of problem.
The team envisions that their solution could contribute to lighter, more fuel-efficient aircraft and cars, as well as more efficient wind turbines. On a broader scale, this method could also apply to other layered materials, such as batteries for electric vehicles.
However, the team acknowledges the limitations of current quantum hardware, which poses challenges in scaling up their solutions. While quantum methods hold great promise, only small-scale demonstrations have been possible so far. “One of the biggest challenges was finding a problem that was well-suited for quantum computing methods while still meeting industry requirements,” they said.
Looking ahead, TU Delft is optimistic about quantum’s role in the automotive and aerospace industries. In the next decade, quantum computing could facilitate innovations in materials simulations, leading to new discoveries for more efficient and sustainable transportation technologies. As for the immediate future, the team plans to further refine their quantum methods, benchmark them against traditional approaches, and explore real-world applications for their solution.
LightSolver: All-Optical Quantum-Inspired Computing
Kathrin Lotto Shabat, a marketing leader at LightSolver, said the team took a different approach to the Quantum Solvers category of the challenge. They didn’t just want to take part in the challenge; they also wanted to challenge themselves as an organization.
LightSolver, a developer of an all-optical, quantum-inspired computing platform, saw the Quantum Mobility Quest as an opportunity to showcase their unique technology and benchmark it against other players in the quantum realm.
“Participating in the Quantum Mobility Quest was a natural fit for us,” Shabat said. “As a company that develops quantum-inspired solutions, we’re always looking for opportunities to demonstrate our platform’s capabilities in real-world applications. We’ve had extensive experience working with clients in the aerospace and automotive industries, so this competition presented a perfect opportunity to apply our technology in a challenging and impactful context. We were particularly motivated by the chance to present our solution to a wider audience and to see how we stack up against other quantum technologies.”
Initially, LightSolver considered focusing on the quantum logistics track, given its familiarity with combinatorial optimization problems. However, they ultimately decided to challenge themselves with the “Golden App” track, which involves heat simulation.
“Logistics is an area where quantum and quantum-inspired solvers have already demonstrated significant potential,” said Shabat. “We’re very familiar with the operational research challenges in this field—traffic routing, dispatching, job scheduling, production optimization—these are problems that every manufacturer and operator faces, and our team has considerable experience addressing them. But we wanted to push ourselves beyond our comfort zone. After bouncing ideas off experts from Airbus and BMW, we decided to tackle heat simulation, which is widely used in the aerospace and automotive industries to understand and manage thermal behavior. These simulations are incredibly compute-intensive, challenging even the most advanced HPC systems.”
LightSolver’s platform, which is all-optical and powered by lasers, processes computations in parallel at the speed of light, offering a novel approach to tackling these compute-intensive workloads. “The mathematical operations underlying heat simulations involve solving linear equations, which differ from the combinatorial optimization problems we usually solve,” Shabat said, adding, “However, our platform is uniquely suited to this task because it processes data faster than silicon-based solutions. We’re excited to demonstrate how our novel compute paradigm can address these challenges, offering new possibilities for simulation and modeling in the aerospace and automotive industries.”
The company envisions that their solution could have a significant impact on the future of transportation. They see the platform will not only optimize logistics but also enhance manufacturers’ simulation and modeling capabilities, leading to safer and more innovative developments in the transportation sector. This increased efficiency in complex simulations enables the creation of better, safer and more sustainable products.
Looking ahead, quantum computing could play a critical role in the automotive and aviation industries over the next decade, according to Shabat.
“These industries have a pronounced need for advanced computing capabilities, and they’re also strongly committed to sustainability goals,” said Shabat. “We believe that the automotive and aviation sectors will be early adopters of quantum and quantum-inspired computing technologies. We’ve been collaborating with world-class research teams in both industries, and we have no doubt that they will be at the forefront of practical quantum usability, blazing the trail for other industries to follow.”
A Future Shaped by Quantum Mobility
The Airbus-BMW Quantum Mobility Quest has brought together some of the most promising quantum technologies aimed at transforming the transportation sector. From structural simulations to materials design, the solutions proposed by CogniFrame and TU Delft showcase how quantum computing can address the scalability and complexity challenges inherent in today’s advanced simulations.
As quantum hardware improves, it could be that these teams’ innovations could one day lead to significant advances in the sustainability and safety of future vehicles and aircraft.
