Currently, 10% of all potential uses for quantum computing being researched have the potential to benefit the automotive industry.
The advancements in QC technology have caught the attention of the automotive industry, as it offers the potential to enhance various processes along the value chain through computational enhancements. A number of OEMs and tier-one suppliers have already initiated studies to explore the potential advantages of QC technology, such as optimizing routes, improving fuel-cell efficiency, and enhancing material durability, to address current industry challenges.
The first pilot use cases are being showcased by several companies. Volkswagen, in partnership with D-Wave, has demonstrated a traffic management system that optimizes the travel routes of nine public transit buses during the 2019 Web Summit in Lisbon, Portugal. Similarly, Bosch, a tier-one supplier from Germany, has invested in Zapata Computing, a quantum start-up from Cambridge, Massachusetts, contributing to a $21 million Series A funding round.
Despite the potential for quantum computing (QC) in the automotive sector, which could amount to billions of dollars in value, Original Equipment Manufacturers (OEMs) and other stakeholders are facing obstacles. The novelty of this technology, combined with the relatively small market that has emerged thus far, has prevented many automotive players from developing a clear QC strategy. To support them, we have examined the maturity of QC and its potential in the automotive sector, as well as identified opportunities and potential next steps for automotive stakeholders.
Quantum computing is undeniably advancing, but widespread implementation is still five to ten years away. The industry has categorized QC into four phases with specific milestones for each. The first milestone, achieving quantum supremacy, was likely attained in 2019.
The initial manifestation of quantum advantage will entail the creation of functional scenarios that will likely simulate quantum phenomena. Various pilots, including Volkswagen's traffic optimization, are presently emerging to demonstrate the first instances of quantum advantage. By 2035 or beyond, intricate problem-solving endeavors that necessitate the collaboration of multiple qubits will become feasible.
By achieving significant quantum advantage, investing in programming quantum computer software for solving specific issues will become commercially feasible. Experts predict that this milestone will be reached in approximately 2030. The ultimate stage is the development of a complete, universal quantum computer equipped with quantum memory and random-access memory, also known as the quantum Turing machine. This machine can run on any number of qubits and execute any algorithm. It should be possible to achieve this within one to two decades.
It is unlikely that QC will completely replace current high-performance computing (HPC) even in the long term. Moreover, the initial attempts at creating value through QC devices will not rely on their ability to solve entire problems on a large scale. Instead, we anticipate that successful QC applications will heavily rely on hybrid approaches in the next decade. Specifically, an optimization problem will be solved by a small QC subroutine which will generate a preliminary solution. The solution will then be refined using a narrower set of variables by a conventional HPC. This approach will allow programmers to leverage early-stage QCs for more efficient execution of HPCs.
The automotive industry is expected to reap significant benefits from quantum computing (QC), with approximately 10% of potential QC use cases already identified as relevant to the sector. By 2025, QC is projected to have a substantial impact on the industry, with the primary value pools lying in solving optimization problems and accelerating learning in autonomous-vehicle-navigation algorithms. The economic impact of related technologies is expected to reach between $2 billion and $3 billion by 2030. In addition to optimizing vehicle routing and processes, QC is anticipated to contribute to enhancing the security of connected driving and supporting the transition to electric vehicles.
Between 2025 and 2030, midterm plays are expected to focus on a few key areas, including quantum simulations that will tackle complex partial differential problems such as heat and mass transfer, fluid dynamics, and compressible flows. Additionally, simulating material properties on an atomic level will become increasingly relevant for optimizing the selection and development of battery and fuel-cell materials.
There will also be a greater emphasis on more complex optimization problems that will involve higher degrees of freedom, such as minimizing the likelihood of supply-chain defaults, optimizing traffic flow in large cities, or solving large-scale multimodal fleet-routing issues.
Finally, complex quantum AI/ML applications will be able to process even larger amounts of data, leading to novel control processes by identifying new variable correlations, enhancing pattern recognition, and advancing classification beyond the capabilities of current HPC clusters.
Starting in 2030 and beyond, the utilization of quantum computing will gradually expand with the help of universal quantum computers. Consequently, algorithms designed to crack common encryption keys, such as prime factorization, will become widely accessible. As a result, efforts will shift towards enhancing digital security and minimizing risks as stakeholders strive to prevent the interception of communications in autonomous vehicles, on-board electronics, and the Industrial Internet of Things. Furthermore, shared-mobility fleets' cloud-based navigation systems will enhance their coverage algorithms by consistently training via quantum computing.