BMW and Pasqal have entered a new phase of collaboration to analyze the applicability of quantum computational algorithms to metal forming applications modeling.
Car manufacturer BMW and quantum computing technology developer Pasqal have entered a new phase of collaboration to analyze the applicability of quantum computational algorithms to metal forming applications modeling.
The automotive industry is one of the most demanding industrial environments, and quantum computing could solve some of the key design and manufacturing issues. According to a report by McKinsey, automotive will be one of the primary value pools for quantum computing, with a high impact noticeable by about 2025. The consulting firm also expects a significant economic impact of related technologies for the automotive industry, estimated at $2 billion to $3 billion, by 2030.
Volkswagen Group led the way with the launch of a dedicated quantum computing research team back in 2016.
BMW has been working with Pasqal since 2019 to develop quantum enhanced methods for chemistry and materials-science in the field of battery R&D, Benno Broer, CCO at Pasqal, told EE Times Europe.
The current collaboration, however, follows the BMW Group Quantum Computing Challenge in late 2021. The contest focused on four specific challenges where quantum computing could offer an advantage over classical computational methods, and Qu&Co was the winner in the category “Simulation of material deformation in the production process”. Qu&Co and Pasqal later merged their businesses, combining Qu&Co’s robust portfolio of algorithms with Pasqal’s full-stack neutral-atom system to accelerate the quantum path to commercial applications. The united business is known as Pasqal and located in Paris.
“The reason we were chosen is because our proprietary method to solve complex differential equations is currently the only realistic method to solve such problems on near-term quantum processors,” said Broer. “The material deformation problems we will now work on with BMW Group are governed by such differential equations.”
Pascal said its team of researchers has developed a digital-analog implementation of its quantum methods, tailored for its neutral-atom quantum processors, which makes these applications “30 times more efficient” than on competing superconducting quantum processors.
When asked to provide more details on this digital-analog approach, Broer explained, “Our approach requires us to create a significant amount of quantum entanglement between our qubits. Intuitively: the more entanglement we create the more powerful (more accurate) our method becomes. In a fully digital implementation, we create this entanglement by applying 2-qubit gate operations (which entangle 2 qubits). In the digital-analog version of the algorithm, we replace this entangling operation by an analog operation, which is a multi-qubit operation. The replacement of the 2-qubit gates by this analog multi-qubit operation makes the method much more efficient, and at the same time more noise robust.”
“The result is that we can generate much more entanglement in the time we have before the quantum processor becomes decoherent (it loses its quantumness due to the inherent noise in all current day quantum processors). And again: More entanglement means a more powerful solver.”
Pasqal’s digital-analog approach is described in more detail in the blogpost, Neutral Atom Quantum Computing for Physics-Informed Machine Learning.
The simulations will run in Pasqal’s facilities over a six-month period.
As to when the first car models optimized with Pasqal’s simulations will hit the roads, Broer said it is too early to tell. “What we can say is that Pasqal expects to be able to showcase the first industry relevant quantum advantage with our differential equation solvers in 2024. We cannot yet guarantee that those first quantum advantage showcases will be for the application of materials deformation.”
Real-world applications of these simulations include crash testing and accelerated development of new, lighter, stronger parts and materials that ensure passenger safety while reducing emissions and development costs, the company said.
The reduction in development costs that Pasqal’s simulations may allow BMW to achieve cannot be quantified at this point of time, said Broer. “In general, we see a trend towards replacing costly and time-consuming build-and-test cycles in automotive R&D with digital research (creating ‘digital twins’ of the car or car parts). The financial benefit related to this should be quantified in both the cost saved for the physical build-and-test process, the cost of the material saved (using less metal while maintaining the same structural strength), and perhaps most importantly the significantly improved time-to-market of a new generation of cars.”
He added, “Our quantum methods provide the required extra computational power to enable accurate ‘digital twin’ type simulations of larger and more complex parts of a car or perhaps someday a full car.”
Pasqal’s quantum computational simulation, now applied to cars, could be used for other sectors. For each new class of differential equation problems, Broer said Pasqal has to parameterize its quantum algorithms to be able to solve that specific class. “Once we can solve the problem of material deformation, we can use these solvers to also tackle problems outside of this field where the differential equations have a similar structure.”
This article was originally published on EE Times Europe.
Anne-Françoise Pelé is editor-in-chief of eetimes.eu and EE Times Europe.