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 datasets; 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.
In the near term, companies may generate the most impact by relying on a hybrid operating model in which they apply HPC to some problems and reserve QC for select cases where it offers the greatest benefits. Consider chemical interactions. Since quantum mechanics govern how atoms and subatomic particles interact, QC could model this intrinsically quantum process with much less memory and processing power than that required by HPC.
Current and future QC mobility applications
QC can promote improvement along the entire mobility value chain (Exhibit 2). Within manufacturing, for instance, OEMs can use QC to optimize the creation of digital twins, which are virtual representations of factories that can help companies optimize robot paths within warehouses, schedule jobs, place equipment, enhance quality control, or improve energy usage. A leading global automotive supplier has entered a partnership with a QC company to build digital twins that will provide greater insight into equipment performance and production processes while potentially reducing waste and energy use.
Some of the most important QC use cases involve engineering R&D and product design. Consider battery technology: almost all EV batteries now contain lithium, often in combination with other chemicals, and manufacturers are investigating other options. QC could improve the algorithms for analyzing molecular structure and interactions, allowing manufacturers to create lighter, safer, and more cost-effective batteries.
OEMs have already formed partnerships with QC companies to accelerate battery development. Some companies are starting to create QC algorithms that use density functional theory to analyze the electronic structures of different materials much more rapidly and accurately than is possible with HPC. Beyond battery development, OEMs could use QC to gain more detailed and rapid insights about chemical properties and reactions during the development of green fuels or heat-resistant materials.
QC could also transform multiple areas of vehicle, aircraft, and satellite design, allowing OEMs to optimize product weight, sensor positioning, and resistance to physical stress. It could also improve computational fluid dynamics testing. These efforts could potentially costly build-test-improve cycles for metal-forming processes because QC can provide insights more rapidly than HPC. Again, multiple OEMs are off to an early start by working with QC companies on design issues to leverage quantum processors and tailored simulation algorithms to predict how any process modifications could alter the composition of vehicle parts and determine whether the changes would make the components noncompliant with regulations. In addition to expediting development cycles and increasing safety, the virtual modeling could help OEMs produce lighter, more fuel-efficient vehicles.
Applications in the broader mobility ecosystem
QC could potentially benefit all players in the mobility ecosystem, including fleet providers and the start-ups that are trying to disrupt transportation.
Fleet optimization. QC algorithms can optimize fleet management, resulting in more efficient operations, reduced costs, and a better environmental footprint. Some trucking fleets now use QC to analyze the effect of multiple variables, including road construction, when planning routes and dispatching drivers. Within maritime, a leading global oil and gas company is working on a QC initiative that involves modeling the comparative advantages of different shipping routes to increase the rate of just-in-time energy delivery. A global OEM has signed a deal to use NASA’s quantum computer in its autonomous-car research and hopes to plot the most efficient routes for diesel commercial truck fleets within regions that contain multiple cities.
Traffic-flow simulation. Road congestion is an increasing problem in many countries, and Volkswagen, in combination with D-Wave Systems, has attempted to develop traffic flow optimization algorithms. In one project, it analyzed data from public taxis in Beijing to determine the best routes between the city center and the airport. Volkswagen then created a mobile app that can provide the best route to any destination. In Lisbon, Volkswagen equipped public buses with a system that uses QC to calculate the fastest route in near real time.
Fuel and cargo loading. Many aviation companies have set ambitious sustainability targets because air traffic is responsible for a high percentage of global emissions. They are also trying to reduce supply chain bottlenecks, which have become more common since the pandemic, by streamlining processes and reducing transit time. QC can help in both areas. For instance, a leading global aerospace company is working on QC algorithms that increase loading efficiency and optimize cargo distribution on flights—improvements that could reduce both time and costs. These algorithms consider multiple variables that affect payload capacity, including the aircraft’s center of gravity and fuselage shear limits.
Air-traffic management. Advanced air mobility involves the flight of next-generation concepts, including passenger drones. One organization, the Quantum Transformation Project, is already attempting to optimize flight routes and scheduling for these aircraft by using QC to assess numerous factors in combination, including constantly changing weather conditions.
Vehicle and infrastructure maintenance. By processing vast amounts of data from sensors and monitoring systems, quantum algorithms can detect anomalies and predict maintenance requirements before critical failures occur. This proactive approach can improve safety, reduce downtime, and enhance overall operational efficiency.
Making the move to quantum computing
Compared with other innovative technologies, investment in QC is still relatively low. It has steadily grown in recent years, however, primarily because various governments have directed large amounts of public funding to it. The QC equity investment in 2022 was about $2 billion, compared with $5 billion for generative AI and $16 billion for immersive-reality technologies.5 Companies clearly recognize QC’s potential value, however, and are considering possible QC applications. As they explore the potential cost and speed advantages, they should consider the following factors:
- Algorithm complexity. QC may have an intrinsic speed advantage because its parallel-processing capabilities reduce the number of unique calculations required.6 For instance, QC can analyze information from unstructured databases more quickly than HPC.
- Execution time. Total execution time is highly dependent on hardware and architectures. Although QC algorithms may find answers using fewer calculations, their execution time might not be faster than that of HPC because of differences in gate speed, read-in/read-out time, and other factors. Hardware companies are now prioritizing improvements in QC execution speed, although these will become less relevant in the long term because improvements in QC’s parallel processing ability will ultimately give it an advantage.
- Energy requirements. HPC can consume up to 25 megawatts of power, which may increase costs and interfere with an organization’s sustainability goals. Energy consumption for QC may be negligible compared with this, as most of it is related to cooling systems.
- Capital and operational expenditures. QC may be more expensive than HPC over the short term because of the infrastructure and materials required. For instance, many of essential QC components are not yet produced on a large scale, increasing costs. QC could become more cost-competitive as the sector matures and more downstream suppliers become available.
For those companies that want to accelerate their QC efforts, three activities may help. First, technology monitoring and capability building are essential. Companies could build dedicated teams to track the latest QC developments, including those related to hardware, hybrid integration, coding platforms, and postquantum cryptography protocols. In addition to accelerating progress, the presence of a QC-focused group may signal this technology’s importance to the entire organization.
The next activity is both practical and basic: use case identification. Over the next three to five years, companies may focus their attention on projects involving noisy intermediate-scale quantum. The computers involved will have between 50 and a few hundred qubits, and their performance may surpass HPC in many instances, but the “noise” in the quantum gates will limit the number of sequential calculations that can be performed before errors arise. Over the longer term, companies may benefit from fault-tolerant QC, which could emerge before the end of the decade. This type of computing minimizes the errors that can occur when qubits interact.
The last key activity is one familiar to many OEMs: collaborating at scale with QC companies and academic institutions. Active participation in such partnerships may help ensure that some QC research focuses on areas that are critical to mobility, and it may help increase the talent pipeline for those companies that want to bring QC skills in-house.
As the mobility sector undergoes changes not seen for decades, QC could be essential to achieving an intelligent, connected ecosystem that relies on the swift exchange and processing of massive amounts of data. While the journey to capture QC’s full potential may seem long and protracted, the technology could potentially shave years off the development timelines for autonomous vehicles and other innovations that might otherwise never materialize or else take decades to achieve.