BMW has signed a deal with quantum computing business Pasqal that will see the automaker use its quantum processors to enhance its manufacturing processes, including potentially removing the need to build prototypes of new products and simulate new materials. The potential of such use cases to save time and money have made automotive companies some of quantum computing’s most enthusiastic early adopters.
Pasqal will work with BMW to eventually simulate complex problems such as paint shop orders and the impact of wind on materials. The French company says that, once up and running, its quantum processor will allow for predictive and rapid virtual modelling, which BMW hopes will lead to safer designs, more sustainable products, and cut out the need to produce prototypes.
Currently new materials and products go through a costly build-test-improve cycle, but Pasqal says its system can produce simulations so accurate that this would no longer be required.
Simulations will include real-world applications such as “crash testing and accelerated development of new parts and materials which are lighter and stronger,” said Georges-Olivier Reymond, CEO of Pasqal, who added that the work would also include predictions for “keeping passengers safe while both reducing emissions and cutting development costs”.
Will quantum computing help BMW?
However, the point at which any of these tasks can be carried out by a quantum machine is some way off. Pasqal itself says it is two years from ‘quantum advantage’, the point where quantum computers outperform traditional supercomputers and are able to carry out these sorts of complex equations efficiently.
Jean-Francois Bobier, partner and director at Boston Consulting Group, told Tech Monitor the gap between quantum and classical machines is unlikely to be closed until at least 2025, and only then in a limited number of commercial applications.
“What we define as quantum advantage, where we have it faster than classical, isn’t happening today and if anybody tells you they have an advantage, they are wrong, it is untrue,” he says. “The best quantum computer is a classical computer simulating a quantum computer, but we predict we will have one or two or three use cases with quantum advantage by 2025, at which point it will be commercially viable.
“When quantum advantage arrives the adoption will be pretty quick as you don’t need to procure a computer and put it in a data centre. They are already being put in place and can be upgraded as the technology evolves.”
So why are businesses forming these partnerships now? Bobier says companies such as BMW are preparing so that they can get the benefit from quantum computing quickly, as small improvements can lead to big cost savings.
Automotive companies are becoming quantum computing’s early adopters
Enthusiasm for quantum computing is growing among businesses, despite the technology being in its infancy. A study by Capgemini, released last month, found that 23% of organisations polled are either working with, or are planning to work with quantum technologies.
The automotive industry is seen as a potential early adopter. The Capgemini report suggests quantum computing could lead to ‘better materials design, entry into new mobility markets,’ as well as crash simulations, battery manufacturing and supply chain optimisation as areas of use.
As part of the research, Joydip Ghosh, quantum computing project lead on Ford’s research and advanced engineering team, explained how the automotive giant plans to deploy quantum processing. “We have been experimenting with solving optimisation problems such as vehicle route optimisation and classification using quantum machine learning. Quantum speed-up has big potential in these areas for the automotive industry,” Ghosh said.
BMW and Ford are not the only automakers to invest in quantum. Volkswagen set up a dedicated quantum team in 2016, and Daimler is among other companies that have been experimenting with the technology.
The main initial quantum use case for many car makers will be modelling outcomes in a way that removes the need for real-world testing and prototypes, BCG’s Bobier explains. The computers could be particularly useful for solving nPr, or permutation, problems, where there are multiple components which can be combined in different ways to generate different outcomes.
For car companies, this is particularly relevant as they need the flexibility to implement a wide number of customer requests – from the type of fuel and driver side, through to the paint and materials used in the dashboard, Bobier says.
“Take the example of a paint job in the factory,” he says “The sequence you put the car into the paint shop is an nPr problem as there is an optimal sequence where you don’t have to change the order of the paints.
“This problem can be solved to a certain extent using classical computers, but there is an error rate, and studies have shown that based on the current error rate, switching to quantum computing could save up to $10 per car.”
When you add in all the other choices, as well as savings gained from accurately modelling how to best cut a piece of leather, wood or glass, the savings begin to add up significantly.