They have to be. Their factories produce much of the world’s critical aerospace infrastructure: satellites, defense systems, spacecraft, and commercial jets. Many of their most complex engineering projects rely on ply composites. These are featherlike, hard-wearing, layered structures that Boeing assembles into wings, fuselages, or other aerospace components as needed.
Designing ply composites turns out to be a complex problem on its own terms, beyond the capabilities of classical supercomputers to solve. Today, Boeing engineers solve this problem by breaking it up into smaller pieces.
Boeing’s partnership with IBM Quantum revealed a new quantum approach that they hope will cut through that complexity. While today’s quantum computers aren’t yet big enough to help design the next airplane wing, the two companies have taken an important step toward that future.
“It shows us that it’s not if quantum computers will be relevant to our business problems, but when,” said Jay Lowell, Chief Scientist for Boeing’s Disruptive Computing and Networks team.
Boeing’s ply design problems can involve up to 100,000 variables, far beyond the capabilities of classical supercomputers to handle.
Boeing and IBM Quantum ran a 40-variable model problem on a quantum computer, then the largest execution of its kind ever.
Ply composites are complex because of how they are assembled.
Each composite is made from thousands of individual plies, which are long strands of super-strong materials. Precision machines layer the fibers one on top of the other in layers, like big looms weaving rocket ships and airplanes instead of fabrics. The machines extrude each layer at a different angle determined during the engineering process.
Those varied angles are important because each ply is only strong in the direction in which it’s laid.
“We need to create a stack of materials that lay in multiple directions on top of each other so that we get strength in all the possible directions that we need it,” Lowell said.
Adding to the complexity of the task, aerospace design places strict boundaries on the thickness and weight of the composites. Boeing’s ply composite design problems routinely involve between 10,000 and 100,000 variables, which is another way of saying they are computationally complex.
“That is well beyond the capability of classical computers today and we expect it will be beyond the capacity of classical computers for some number of years,” Lowell said.
Today, Boeing breaks up its ply composite problems into smaller pieces that classical computers can handle. Then they bring all those results together – following strict design rules – to gain solutions to the whole problem.
This approach is effective. It leads to safe, strong composites that Boeing can use for its airplanes. But there are costs.
“If you want a long, straight line of composite,” said Joel Thompson, Associate Technical Fellow at Boeing, “it makes sense to lay down one long, straight line of ply, rather than laying down a tiny bit of ply, cutting it, laying down the next piece of ply, cutting it, and so on.”
That’s a consequence of the approach where components are designed in bite-sized pieces. It makes the process take more time, effort, and money.
“We’re interested in looking at other approaches for solving this kind of problem,” said Marna Kagele, Technical Fellow at Boeing.
Boeing hopes that quantum computers will eventually help streamline this kind of complex problem solving. A quantum computer may one day solve problems with thousands of variables all at once, without breaking it up into bite-sized pieces.
As a first step, IBM Quantum and Boeing researchers built a model version of the ply composite problem to test the idea. They stripped the problem down to its essence: finding the optimal way to stack layers of material on top of one another. Call it the cutdown ply composite problem.
With quantum computers still developing, solving this cutdown problem using real quantum hardware was a challenge. Existing quantum optimization methods did not use quantum resources efficiently enough.
When Boeing and IBM Quantum began their work together, their toolkit of standard quantum optimization algorithms could encode just one binary variable – representing a 1 or a 0 – for each qubit.
(Qubits are fundamental units of quantum computation roughly equivalent to binary bits, which form the 1s and 0s in a classical computer.)
To design a complete airplane wing, you need to account for thousands of variables – representing layers of ply as well as the rigorous engineering rules Boeing follows to build strong airframes. The cutdown ply composite problem involves 40 variables.
Qubits are precious resources in today’s quantum computers, which aren’t yet big enough to match one qubit to each of those 40 variables. That limitation forced innovation, Kagele said.
IBM Quantum brought some of its own internal work on quantum algorithms to Boeing, which the teams used together to develop a new approach to quantum optimization. Rather than encoding one variable to each qubit, the team showed it was possible to encode three binary variables to each qubit. So a single qubit could handle three times the information load of a classical bit – and represent three times as many variables.
That was a radical increase in efficiency even relative to earlier quantum optimization algorithms. It led to a successful run of the cutdown ply composite problem on a real IBM quantum computer. With 40 binary variables, the team ran what was then the largest binary optimization problem ever handled by a quantum computer, nearly doubling the previous record.
“We’ve parsed a very large optimization problem that is core to the design of our products, and shown that quantum computers can address a fraction of that optimization problem but do it well,” Lowell said.
There are still several years of work to do before Boeing will use quantum computers in its design process, he added.
“We need quantum computers to be larger and handle larger optimization problems than they can today,” he said, “but these new methods allowed us to get much further than we expected when we started this project. The solution seems closer than we expected even a couple years ago.”
Beyond the specific implications for quantum optimization or the ply composite problem, Kagele said the process of collaboration with IBM Quantum has gotten Boeing ready to tackle quantum challenges head-on.
“Our partnerships with clients like Boeing are helping us push the frontier of quantum research,” said Jennifer Glick, Technical Lead for Quantum Prototypes at IBM Quantum who worked on this research with the Boeing team. “Through this work, we are beginning to see what a future where quantum computers solve real, practical problems looks like.”
The relationship between the two teams began with IBM Quantum mentoring Boeing’s researchers. But that support enabled Boeing to quickly improve its internal skills.
“You can imagine how fast your learning can progress when every time you hit a roadblock or something you’re not sure about in your learning journey you have someone to ask with more experience,” Kagele said.
That mentorship matured into a collaboration, which led to their groundbreaking work.
“Our main goal in standing up this project is to help our business understand how to transition from doing things classically to doing them in a hybrid with quantum methods,” Lowell said. “We’ve stood up a team that is capable of doing that, we’ve developed internal tools that will make working on the next problem easier.”
With that team in place, IBM Quantum and Boeing are already exploring new ways that Boeing can get value out of quantum computing. One area of interest: developing advanced corrosion-resistant chemicals to coat airplanes. As Boeing builds its quantum workforce and quantum computers improve and scale, expect the company to apply quantum problem-solving to more aerospace challenges.
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