Automotive and aerospace companies are actively exploring how quantum could improve CFD, digital twins and more.
From hardware to software, the race is on to develop the systems that will support quantum computing innovation in all industries. The technology has been in the works for decades, and it’s advanced to a point that many companies are testing out how quantum computing could impact engineering.
“It’s not a matter of if, it’s a matter of when. We just need to scale up the hardware to get there,” head of technical marketing at quantum software company Classiq, Erik Garcell, told Engineering.com.
Let’s take a look at how quantum computing could impact engineering applications from simulation to digital twins.
Quantum computing in automotive and aerospace
Quantum computing promises tantalizing advantages over classical computing: the ability to solve previously untenable problems, to more accurately simulate quantum mechanics, and to operate at mind-boggling speeds. But this promise has yet to be delivered.
“There is no advantage to quantum computers right now,” Garcell said. “But it’s [part of] a digital transformation roadmap.”
As companies ranging from IBM to Google work to develop more capable quantum computers, the industries poised to use them are paying keen attention. Director of quantum algorithm engineering at Nvidia, Elica Kyoseva, told Engineering.com that many organizations are already investing in seeing how greater scale quantum computers could fit into their engineering workflows.
The automotive and aerospace industries are looking to use quantum computing for simulation, materials development, battery research, route optimization and more.
“A very actively researched area is realizing digital twins of complex chemical materials, which have applications in battery design for electric vehicles, or other areas in engineering that require advanced materials,” Kyoseva said.
BMW, Volkswagen and Rolls Royce have already begun testing quantum computing.
BMW conducted a trial with Nvidia to showcase how Nvidia’s cuQuantum SDK could accelerate quantum circuit simulations to improve generative modeling algorithms. So far they are reporting improved training time of quantum generative models. BMW has also worked with Classiq to explore how quantum algorithms could optimize mechatronic systems.
Volkswagen launched a quantum computing research team in 2016. In partnership with quantum providers including D-Wave and Google, Volkswagen has explored several applications of quantum computing in the automotive industry—such as in the paint shop. The team developed a quantum algorithm designed to maximize the efficiency of applying different primer types without slowing the overall assembly process.
“Challenges like these may sound simple, but in some cases would require near-supercomputer levels of power to solve with traditional hardware,” said David Von Dollen, lead data scientist for Volkswagen, in a 2021 company blog post.
Last year Rolls-Royce claimed it had developed “the world’s largest quantum computing circuit for computational fluid dynamics (CFD)” in partnership with Nvidia and Classiq. That circuit could potentially help Rolls-Royce better simulate the performance of its jet engines.
“It’s a matter of getting better, finer, more accurate simulations,” Garcell said.
Quantum digital twins
Digital twins are another engineering application that could receive a quantum boost. The computing paradigm could enable more detail and complexity in these virtual systems. Nvidia is even working on creating digital twins of quantum computers themselves to advance the technology.
“Some problems are fundamentally very hard to solve by classic computers. Examples are creating digital twins of complex material simulations or solving large optimization problems which include risk analysis or routing or other scheduling tasks. This is where we actually feel AI and quantum computing can make a difference,” Kyoseva said.
Garcell agrees that there will be a big focus on materials when quantum tools catch up.
“It’s easier to simulate quantum mechanics on a quantum computer than a classical one. And that deals with materials development,” Garcell said. “How do you come up with higher strength ratio materials? At some point when you’re trying to figure out how to put these materials together you have to worry about simulating the quantum mechanics of it.”
Lowering the quantum barrier
Despite there being some big new problems that quantum can conquer, the average engineer likely won’t have to change their day-to-day workflows. At least, that’s what Nvidia is hoping for.
“It will not be something [engineers] will have to be particularly aware or make a conscious effort to adapt to this new type of computing,” Kyoseva said.
Software should automatically take care of routing computing tasks to the right chip, classical or quantum. Engineers wouldn’t need to be trained in how the chips process the information. They would just see the results.
“Our goal is that this work will transcend to every engineer’s workflow. It will enable them to be more efficient, let’s say to do much higher throughput of calculations and simulations,” Kyoseva said. “But at the same time, this will be done in the background.”
This ease of adoption is what quantum companies are relying on. Companies like Classiq and Nvidia are developing the software frameworks and infrastructure to pair with the hardware when it is ready to go. Once the technology catches up with our computing ambitions, they want you to be ready to pick it up for your simulations without a second thought.
That lower barrier to entry is already looking promising. “We see more and more interest in people coming from different fields and areas being interested in exploring quantum computing for their industry,” Kyoseva said. “For me I just can’t wait until we see the full potential of quantum computing realized and transforming these industries.”
