The mobility sector—always known for innovation—is now evolving more rapidly than ever. Sales of electric vehicles (EVs) are surging, with demand expected to grow sixfold from 2021 through 2030.¹ New solutions, including leading-edge batteries and autonomous cars, are advancing in development and attracting interest from investors, who have funneled almost $280 billion into automotive hardware and software solutions since 2010.² Meanwhile, consumers who once gravitated to private cars are increasingly exploring greener options, including e-kickscooters and ridesharing services.

The shifts in technology and consumer preferences will eventually result in a mobility ecosystem that is fully connected, intelligent, and environmentally friendly. Vehicles could have software that allows them to scan their surroundings to find parking spaces or identify hazards, perhaps paving the way to full autonomy. Passengers could have access to in-car gaming and video streaming. If a route is blocked, vehicles could propose alternatives. For more complicated trips, consumers may use applications to transfer seamlessly between subways, shared vehicles, and other transportation options.

These changes—as well as many other connected-car features and mobility applications—depend on the rapid and smooth exchange of vast amounts of data between in-vehicle computers and those in other locations. Similarly, many mobility applications, such as those for mapping, depend on the exchange of information between tech companies and OEMs. Many of these businesses have only recently begun to work together, and the partners are still experimenting with the best ways to share and exchange data.

Existing computers, although sufficient for many applications, can’t fully support all of the changes required to create a connected and intelligent-mobility ecosystem. Quantum computing (QC) could potentially provide faster and better solutions by leveraging the principles of quantum mechanics—the rules that govern how atoms and subatomic particles act and interact. (See sidebar, “Principles of quantum computing,” for more information). Over the short term, QC may be most applicable to solving complex problems involving small data sets; as its performance improves, QC will be applied to extremely large datasets.

Although researchers have estimated that widespread commercial application of QC is about five to ten years away, some mobility companies are already piloting applications and building their capabilities in this area. Additional opportunities could soon emerge. For instance, OEMs could use QC to simulate how changing the material composition of a vehicle component would affect performance. QC’s benefits might be particularly helpful in large countries with multiple roads and many route alternatives. Within autonomous driving, QC may improve the performance of sensors and vehicle systems, allowing them to make instant decisions when confronted with an obstacle in the road, or help OEMs to develop better encryption algorithms to prevent hackers from taking over vehicles.

If mobility players continue to scale their initiatives, QC could create significant value in areas ranging from vehicle design to last-mile delivery to long-distance shipping. Our analysis shows that automotive, along with chemical, financial services, and life sciences, is likely to experience the earliest economic impact from QC. In the automotive industry alone, for instance, the economic value of QC could range from $29 billion to $63 billion in value by 2035.³

The benefits of quantum computing

The algorithms used in high-performance classical computing (HPC)—today’s standard—are sufficient for many computations, including those used to evaluate the results of clinical trials, analyze financial trends, and predict weather patterns. QC could handle the same computations much more rapidly, however, and with lower power requirements. QC might also solve some complex problems across industries that are now beyond the reach of HPC.

Within the mobility sector, some companies have been hesitant to pursue QC applications because HPC costs less and is equally likely to solve most problems, albeit more slowly. But this mindset might be shortsighted, since QC algorithms are constantly improving and the associated costs may decrease. Across industries, QC is most likely to gain traction for the three following activities because it offers the greatest time and cost advantages over HPC, as well as better accuracy in some instances (Exhibit 1):

• Optimization. Optimization algorithms consider multiple parameters in various combinations to determine how they affect final outcomes, such as the likelihood that using new materials during manufacturing will reduce waste. In some cases, companies may use classic algorithms to divide a large problem into more manageable chunks and then apply QC to those smaller sections to expedite calculations. Over the short term, QC optimization applications are most likely to generate benefits. For instance, autonomous cars may be on the road with human drivers who don’t always make rational decisions. QC might help autonomous vehicles correctly predict how drivers might react in certain circumstances by analyzing massive quantities of driving data.⁴
• Simulation. QC can enable faster and more precise simulations, such as those that assess the internal energy structure of different molecules and their interactions. Within mobility, simulations could help to optimize battery development, facilitate the creation of heat-resistant materials, and aid the development of alternative aerospace fuels. Such use cases are likely to gain traction over the medium term.
• Hybrid machine learning (ML) and artificial intelligence (AI).QC algorithms can reduce the training time and power requirements for ML/AI models, especially in the most computationally intensive layers, which will help companies reach decisions more quickly. For instance, they might be able to shift flight schedules and routes more rapidly based on data from atmospheric models. Another benefit is that QC might decrease the amount of training data required. It will likely take five to ten years for QC to generate substantial outcomes within AI and ML.

https://www.mckinsey.com/industries/automotive-and-assembly/our-insights/gearing-up-for-mobilitys-future-with-quantum-computing