Study: How the automotive industry will benefit from quantum computing

Business news |
By Christoph Hammerschmidt

After companies such as IBM with its Q System One or D-Wave Technologies made headlines in recent years with supposedly usable quantum computers, various companies in the automotive value chain have taken a closer look at this technology – the promises made by manufacturers were too seductive. According to their pledges, quantum computers are ideal for solving certain problems that the best scientists have long been brooding over, such as route optimisation, fuel cell optimisation and the durability of materials.

According to the McKinsey study, some of these early users have already achieved a certain success. Volkswagen, for example, has teamed up with D-Wave to develop a traffic management system that optimises the routes of buses in urban traffic. The automotive supplier Bosch has invested $21 million in the start-up company Zapata Computing (Cambridge, Massachusetts).

However, the reluctance still far outweighs the commitment to this innovative computing technology, write the authors of the McKinsey study. The novelty of the technology and the still very narrow market have so far discouraged many companies from intensively engaging in quantum computing. It will take another five to ten years before this technology has become established in the long term. By then, quantum computing will have overcome several hurdles: Quantum Supremacy must be achieved; the practical benefit must be proven beyond doubt; application software must be available to solve concrete problems; and above all, a Quantum Turing Machine must be available. The latter means that a universally applicable quantum architecture with quantum memory and conventional main memory (RAM) must be available. Such a machine, as described by the experts at McKinsey, will be able to work with the number of qubits required by the users and execute arbitrary algorithms. Such a machine will be available in one to two decades, the study says.

The automotive industry will be one of the primary value pools for QC, with a high impact noticeable by about 2025. Most of the early value added will come from solving complex optimization problems, including processing vast amounts of data to accelerate learning in autonomous-vehicle-navigation algorithms. In later years, QC has the potential to have a positive effect on many areas in the automotive industry, such as vehicle routing and route optimization, material and process research, and the security of connected driving. Moreover, QC can also provide a boost to automotive players transitioning into the electric-vehicle (EV) era by notably accelerating R&D of novel technologies. For instance, companies can speed the transition of their more traditional technology spectrum towards more relevant technologies such as cooling of EV batteries. Likewise, the simulation of material process research for batteries and fuel cells could be a field where QC could be deployed with a chance of success.

Near-term opportunities for QC – for the time frame through 2025 – are expected to surface in product development and R&D. Relevant use cases will primarily relate to solving simple optimization problems or involve parallel data processing for simple quantum artificial-intelligence/machine- learning (AI/ML) algorithms. These QC applications will be executed as part of a hybrid solution, where bits of a larger problem, processed by a High-Performance Computer (HPC), are outsourced to a quantum computer and results are fed back into the HPC flow. Possible optimization use cases include the combinatorial optimization of multichannel logistics, highly local traffic-flow optimization, and improvements in vehicle routing. Quantum AI/ML might involve the time-efficient training of autonomous-driving algorithms due to an increase in the parallel processing of large amounts of data.

In the midterm – that is, the timeframe from 2025 through 2030 – the authors expect the QC activities in the automotive industry will focus on things like simulations (heat and mass transfer, fluid dynamics as well as material properties at the atomic level – relevant for the development of battery and fuel cell materials). In addition, more complex city traffic simulations could become possible as well as solving large-scale multimodal fleet routing problems. Plus, the capability of more advanced quantum computers to process large amounts of data will help engineers and developers to implement solutions for enhanced pattern recognition.

In the long run, that is from 2030 onward, quantum- computing applications will build on at-scale access to universal quantum computers, the experts estimate. Prime factorization algorithms to break common encryption keys will therefore be universally available. The focus will likely move toward digital security and risk mitigation as players try to prevent the quantum hacking of communications in autonomous vehicles, on-board electronics, and the Industrial IoT. Cloud-hosted navigation systems of shared-mobility fleets will improve their coverage algorithms through regular training enabled by quantum computing. Other promising fields of application include investigating and optimizing crash behaviour, cabin soundproofing or training for AI-based autonomous driving algorithms.

Such applications require a steep maturing process for the QC industry. The McKinsey experts admit that today it is unclear how the QC hardware industry will be able to reach this degree of maturity, but they see a number of possible ways. For instance, existing QC approaches will evolve over time. QC will establish itself as a cloud service, which will relieve users of the need to acquire and run their own hardware. Similarly, the market researchers expect that the QC software will evolve – with the difference that in contrast to hardware where almost all competencies are concentrated in the US, European players will also become relevant.

While many uncertainties persist, the analysts express optimism that most of the problems ahead will be solved within the time frame specified. For 2030, they expect an economic impact of these technologies in the automotive industry alone of some $2 billion to $3 billion by 2030.

The full study can be found here:

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