Quantum Untangled: What is the automotive industry up to?
Tracking traffic, designing new chemicals for batteries and devising guidance algorithms for future driverless vehicles, that’s what.
The year is 1975, the vehicle a Dodge Tradesman Maxivan with an avocado-green interior, a mustard exterior defaced with custom murals depicting the movement of photons and electrons, and license plates reading ‘QANTUM’ — six letters being the maximum permissible in California, the home state of this particular automobile. Who else, I hear you ask, would be at the wheel of this vehicular monstrosity than the eminent physicist and all-round ‘I’ll-watch-the-Trinity-Test-without-goggles-behind-a-truck-windscreen’ mad lad Richard Feynman. Driving along the coast of Baja California with his family, I doubt he could imagine all of the weird and wonderful things the automotive industry is now doing with his great passion, quantum computing.
In the past century, cars have evolved from simple motorised carriages to autonomous vehicles with advanced electronics and navigation capabilities. But the next leap in innovation will require computational power beyond the ken of even our most advanced classical supercomputers. That's why major manufacturers like Jaguar-Land Rover, Volkswagen, Rolls-Royce, Daimler, Toyota, BMW, and Ford are now looking to quantum computing to accelerate advancements in battery chemistry, autonomous driving, logistics and more.
Last week I looked at how the oil and gas industry could end up using the potential of quantum computing to dig yet more wells and pump ever more carbon into the atmosphere. The opposite is likely to be true of the automotive sector. Instead, its priorities will focus mostly on attaining greater efficiency within vehicles — hopefully without increasing their cost.
Material insight
One major application for quantum computers lies in divining new mysteries in chemistry, and according to a recent report on the automotive industry’s flirtation with quantum by IDTechEx, there’s considerable excitement about how this might be harnessed to discover new chemical formulations for electric batteries.
Quantum computers could also accelerate discoveries in materials informatics which, as far as electric cars are concerned, concentrates on the reactions sparking away inside their batteries. Usually the preserve of classical supercomputers and time-consuming trial-and-error testing, quantum-powered simulations of electron interactions could potentially provide researchers with a more accurate understanding of the reactions taking place at all those anodes and cathodes. This could, some hope, lead to longer lifespans, better energy density and charging speeds for the batteries powering EVs – ultimately making these vehicles less reliant on the charging points that remain erratically distributed throughout the global road network.
Another major area of interest for the automotive industry in quantum computing is the role it could play in logistics. Quantum algorithms can help optimise complex logistical challenges like delivery routing, vehicle scheduling, and factory automation. Quantum techniques like annealing may also be able to optimise automotive assembly lines earlier than other platforms.
The routing of fleets of delivery vehicles is likely to be an early use example. A classical computer can be used to find the optimum route for an individual truck but redirecting multiple vehicles in response to an unexpected situation can be challenging. This is because it would need to recalculate new optimal routes for each truck individually – a feat that’s even supercomputers find extremely taxing. A quantum computer, however, would not only possess the computational heft to find the optimal solution for the entire fleet, but also deftly react to new variables.
Quantum annealing technology pioneered by D-Wave is already providing some support in certain areas of logistics. Volkswagen had a live traffic routing system running on D-Wave annealers that minimise wait times and avoid congestion. Launched as a pilot in 2019, the system was installed on MAN-buses run by public transport company CARRIS in Lisbon, Portugal. The algorithm calculates the fastest route for each individual bus in the fleet and optimises it almost on a real-time basis.
Driverless vehicles
Quantum machine learning is also powering new innovations in autonomous vehicles. A version of AI that uses quantum circuits to map more variables than is possible in a binary system, companies like Hyundai are experimenting with quantum machine learning to create guidance algorithms for self-driving cars. Specifically, that involves finding new ways to encode data into quantum circuits to speed up classification and training to increase the familiarity of the vehicle with typical (and atypical) road conditions, thereby reducing the need to parse immense volumes of sensor data in the moment. Some companies like Quantum Brilliance are even looking to build quantum accelerators small enough to fit on board the car itself, accelerating on-board data processing, too.
Like quantum hardware itself, it will be some time before the automotive companies see any commercial return on this early investment – probably a decade, at the very least. ‘As such, it has come too late to impact the first wave of electrification for the mass market,’ IDTechEx’s report opined. Even so, early investment will give those companies a crucial advantage in the next wave of energy storage and automation advancements. Give it time, and we should see quantum computing help transform the algorithms powering autonomous driving, find new manufacturing efficiencies and trigger an evolution in vehicle design itself. Let's just hope the focus remains on cleaner and more efficient vehicles, and not on ways to make gas guzzling engines faster.
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