How quantum computing could drive the future auto industry
- The overall market value of QC services is estimated to be worth $32-52 billion by 2035
- The economic impact for the automotive industry is estimated to be up to $3 billion by 2030
- Applications include route optimization, materials research, and much more
Quantum Computing (QC) has been gaining huge momentum in the last few years. Recent breakthroughs in affordable technology have seen conversations shift from the theoretical to practical use cases.
As early as 2018, IBM drew attention across the tech world with the creation of its Q System One quantum computer, while D-Wave Technologies went on to announce a QC chip with 5,000 “qubits”, more than doubling its own previous 2,000-qubit record.
While quantum-computing applications may still be five to ten years down the road, a recent report by McKinsey shows that the automotive and transportation sectors have been quick to capitalize on QC’s potential, and have successfully showcased how effective the technology can be with several pilots.
Several OEMs (original equipment manufacturers) and tier-one suppliers are actively discovering how the technology can benefit the industry by resolving existing issues related to route optimization, fuel-cell optimization, and material durability.
Just last year, Volkswagen partnered with D-Wave to demonstrate an efficient traffic-management system that optimized the travel routes of nine public-transit buses during the 2019 Web Summit in Lisbon.
Elsewhere, significant investments have already been made, with German supplier Bosch acquiring a stake in Massachusetts-based quantum start-up Zapata Computing, contributing to a US$21 million Series A investment.
BMW, Daimler, and Volkswagen have announced that they are actively pursuing QC research, including quantum simulation for material sciences, aiming to improve the efficiency, safety, and durability of batteries and fuel cells.
The potential for QC in the automotive sector could translate into billions of dollars in value as OEMs and automotive stakeholders hone in on the market’s niche and develop a clear QC strategy.
As things stand, automotive will be one of the primary value pools for QC and is expected to have an impact on the automotive industry of up to US$3 billion by 2030, thanks to its potential in “solving complex optimization problems that include processing vast amounts of data to accelerate learning in autonomous-vehicle-navigation algorithms.”
QC will later “have a positive effect on vehicle routing and route optimization, material and process research, as well as help improve the security of connected driving,” and help accelerate research into electric vehicles (EV).
Supply routes involving several modes of transport could be optimized using algorithms developed through QC, while other applications will improve energy storage and generative design. QC could also help suppliers improve or refine kinetic properties of materials for lightweight structures and adhesives, as well as develop efficient cooling systems.
QC will be utilized by automakers during vehicle design to produce improvements relating to minimizing drag and improving fuel efficiency. What’s more, QC has the ability to perform advanced simulations in areas such as vehicle crash behavior and cabin soundproofing, as well as to “train” algorithms used in the development of autonomous-driving software. QC’s potential to reduce computing times from several weeks to a few seconds means that OEMs could ensure car-to-car communications in real-time, every time.
Shared mobility players such as Lyft and Uber also have the potential to use QC to optimize vehicle routing, while improving fleet efficiency and availability. Alternatively, QC can help service providers simulate complex economic scenarios to predict how demand varies by geography.
Within the next five years, the automotive industry will continue to focus on product development and R&D.
QC isn’t likely to replace existing high-performance computing (HPC), but will instead rely heavily on hybrid schemes where a conventional HPC can help refine problem-solving more efficiently. A computational problem, for example, to find the most efficient option among billions of possible combinations will initially be iterated with a quantum computer to get an approximate answer before the remainder is handled by an HPC to round off assessments in the subset of solution space.
The pathway for QC is still uncertain despite its potential. Investing in QC is a heavy commitment but will almost certainly put companies ahead of competitors further down the line once it has become more mainstream in use.
Automotive players will need to determine exactly where they fit in the value chain, while building solid partnerships and valuable intellectual property.
The next five to ten years will see players prioritizing application development and building focused capabilities, while making first pilots and prototypes operational. Ten years and beyond will see businesses take full advantage of their technological edge through QC and expand their core capabilities.
As QC continues to make breakthroughs, the automotive sector is set to drive the technology to the next level.