Automotive Engineering - September 2024 - 22

BMW, Classiq
find better architectures through
QUANTUM
COMPUTING
BMW's future electric and mechanical architectures
could be improved through quantum calculations
made with help of Classiq and NVIDIA GPUs.
Everything from electric motors to robot arms can be looked at through
the lens of a powerful algorithm.
by Sebastian Blanco
A
s part of BMW's Emerging Technologies team, Lukas Müller
picks apart new ideas to evaluate if they are relevant for
the automaker. He wasn't the first at BMW to look into how
quantum computing might change the way the German
automaker builds cars, but he told SAE Media that the work he's doing
is just the tip of the iceberg when it comes to harnessing more
powerful computers that will help build the better cars of tomorrow.
In June, BMW announced it would collaborate with Classiq and
NVIDIA to find optimal architecture designs for electrical and mechanical
systems in future vehicles. The idea was to use quantum
computing to develop a real-time solution that would make a vehicle's
architecture more efficient by analyzing a series of potential
motors, batteries, cooling systems and other components by looking
at the drive train as a series of linear equations.
" It's a really complex system, " Classiq's technical marketing manager,
Erik Garcell, told SAE Media. " When you get into the data, you
have to worry about the phase of the power going into these systems,
too, and timing that and making sure it's all good. "
Garcell said any eventual product would be an on-board device that
calculates what to turn on and off in which sequence based on realtime
data. Since scalable quantum systems are not yet available, the
difficult analysis would be done by the quantum computer before the
vehicles are built. Then, simpler onboard systems would use the learnings
to control powertrain devices based on the rules the quantum
system came up with. Garcell said the next step was to apply a quantum
approximate optimization algorithm (QAOA) to the problem.
22 September 2024
" We were trying to optimize the system of linear
equations, that drivetrain we're talking about of electrical
components, " he said. " How do you optimize this
huge and complex neural network? It's not just the
one pass. It's feeding data into itself. You could think
of it almost like a graph theory problem. By optimizing
this using this QAOA algorithm, they're able to figure
out a more efficient system of linear equations which
they can then backtrack out to the original system and
say, this is the more optimal drive train. If this is connected
to this, connected to this, connected to this, in
this way, and the data is feeding to each other in this
way, that would be the most efficient linear equation,
the more efficient electronic drive train that would
more often than not save energy. "
Step one of the whole process was understanding
what quantum computers can and can't do to solve
this problem, according to BMW's Müller. Figuring out
how the robots might shave a few milliseconds off of
their job time - another detail BMW is using quantum
computing to calculate - is different than running
simulations to discover which pipe thickness will
work best or how to optimize the flow of the cooling
liquid inside a vehicle.
" We decided on, as a very basic problem, taking
four components that would exist in a car and asking,
AUTOMOTIVE ENGINEERING
BMW
Automotive Engineering - September 2024

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