Automakers and suppliers shouldn’t dawdle on further delving into quantum computing - Just Auto
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Automakers and suppliers shouldn’t dawdle on further delving into quantum computing

23 Jun 2021 (Last Updated June 23rd, 2021 10:01)

It's not here yet, but quantum computing promises a big leap in computing power and capability to manage huge data sets, including in the automotive sector.

In Lisbon, VW worked with D-Wave on a pilot project for traffic optimisation with a quantum computer.

Over the past year, the journalistic jargon surrounding the automotive industry increasingly presents the vehicles of tomorrow as ‘computers on wheels’. This characterization has no doubt been fuelled by the global semiconductor shortage, which has harshly spotlighted the dependency of the sector on computing hardware.

However, most of the chips affected by the current shortage concern lower-grade specifications, such as microcontrollers and system on chips (SoC) for automotive electronic control units (ECUs) and displays. Fixating on the shortage and the ‘computer on wheels’, as important as they are, may, therefore, actually divert attention from more consequential, high-grade developments in quantum computing (QC) which could revolutionize the automotive industry. The ‘computer on wheels’ metaphor also misses the potential of a computing revolution within and across the industry’s value chain (where investments are also more likely to tangibly pay off).

A nascent but increasingly viable technology

In its recent report on ‘Quantum Computing’, GlobalData details the technology, its value chain, industry trends, and leading players. GlobalData defines QC as machines that use the properties of quantum physics and ‘qubits’ to store data and perform computations; QCs are not general-purpose systems but powerful parallel processing systems for carrying out single, specific tasks like optimization or factorization. QC could also aid in handling the explosion in data volume that automakers face by significantly enhancing the speed of artificial intelligence and machine learning, and therefore making previously unviable problems practicable. For context, former Intel CEO Brian Krzanich said back in 2016 that autonomous vehicles could generate up to four terabytes of data per day.

As an example of QC’s technological superiority, take the travelling salesman problem, which attempts to find the shortest route between several destinations. The number of potential solutions increases exponentially as the number of destinations, or nodes, is increased. At 20 nodes, and at a computational rate of one path per microsecond, a classical computer could take up to two millennia to evaluate every possible path. A recent QC breakthrough directly related to this area occurred in January 2020, when a research team at Tokyo University developed a chip with a quantum annealing processor that could solve a 22-city travelling salesman problem instantly.

Of course, QC is a still a costly and nascent technology, with GlobalData noting that it will likely be at least another five to seven years before intermediate machines become available that can offer a quantum advantage over classical computers in specific optimization applications. However, it may also be possible for QC to work in tandem with existing high-performance computing (HPC) technology, whereby a QC generates an approximate solution for a large-scale optimization problem that a HPC cluster could more comfortably navigate with a narrower set of possibilities to evaluate.

Leading automakers and suppliers are in the early stages of exploring use cases of quantum computing

An early automotive pilot of QC was Volkswagen’s partnership with D-Wave in October 2019 to develop a traffic-management system which optimized, almost in real-time, the individual travel routes of nine public-transit buses in Lisbon (essentially a variant of the travelling salesman problem).VW and others’ focus on QC-based traffic and route optimization shows a recognition of the challenge that the mobility megatrend is presenting to many automakers.

Prior to this, tier-1 supplier Bosch bought a stake in Harvard QC spin-off Zapata in April 2019.Bosch is using QC to conduct complex simulations of, and research into, fluid dynamics, electromechanical systems, and refining predictive maintenance models. Similarly, Daimler has worked with QC leaders Google and IBM to simulate chemical reactions in the pursuit of better battery and fuel-cell systems.

Others, like BMW’s partnership with Honeywell formed in January 2021, are intending to use the technology to maximize, close to real-time, their supply chain efficiency from both a cost and production perspective.

All these examples are just the tip of the QC iceberg. While it’s true the technology is overhyped in some circles, it is most often timeframes, rather than the proposed benefits, which are oversold. The automaker or supplier that has wedded itself most closely to a QC technology player at the point of significant commercial breakthrough is therefore likely to have a significant advantage over competitors.

Now is the time to deepen automotive relationships with quantum computing players

Automakers and suppliers must think about deepening relationships with QC providers or purchasing further stakes in challenger players. Partnerships must begin to extend beyond pilots and enable the development of automakers’ own QC intellectual property. Automakers should also continue to assemble in-house teams that can become well-versed in translating classical problems into quantum ones.

A welcome development in June 2021 is the establishment of the cross-sector Quantum Technology & Application Consortium (QUTAC) in Germany. BMW, Bosch, and VW are among the ten founding members, which was formed at the behest of the German government in the pursuit of a ‘sovereign’ QC ecosystem. The group seeks to develop, test, and share applications that result in usable industrial QC applications. By creating collective demand and sharing the burden of progress, the consortium may help to remove barriers to entry for non-specialist players in the long run.

QC might yet be far from being an automotive megatrend, but the latent signs appear to be there. That makes the potential payoff well worth the risk of investment. Computers on wheels are good, but it’s the computers behind the wheels that will eventually be the real differentiator.

Quantum computing – the basics

Theoretically, quantum computing can complete in seconds tasks that would take classical computers thousands or even millions of years. Quantum computers are machines that use the properties of quantum physics to store data and perform computations. Use cases stretch from improved weather forecasting to cracking the codes used to encrypt all internet messaging. The company (or government) that owns the first at-scale quantum computer will be powerful indeed.

Quantum computers are proving extremely difficult to build, and fully-fledged commercial computers are not expected for 10, 20, or even 30 years. However, within the next five to seven years, intermediate quantum computers are likely to become available that can offer a quantum advantage over classical computers in certain optimization applications across, for example, space warfare, logistics, drug discovery, and options trading. These intermediate devices will become more powerful and robust with successive generations.

GlobalData Report: Quantum Computing – Thematic Research