SUPPLY-CHAIN SECURITYSecuring Supply Chains with Quantum Computing

The Russo-Ukrainian conflict and the COVID-19 pandemic have shown how vulnerable global supply chains can be. International events can disrupt manufacturing, delay shipping, induce panic buying and send energy costs soaring.

New research in quantum computing at Sandia National Laboratories is moving science closer to being able to overcome supply-chain challenges and restore global security during future periods of unrest.

“Reconfiguring the supply chain on short notice is an exceptionally difficult optimization problem, which restricts the agility of global trade,” said Alicia Magann, a Truman Fellow at Sandia. She has led the development of a new way to design programs on quantum computers, which she and her team think could be especially useful for solving these kinds of massive optimization problems someday in the future when quantum technology becomes more mature.

The Sandia team recently published the new approach in two joint papers in the journals Physical Review Letters and Physical Review A. Research was funded by the Department of Energy’s Office of Science, Office of Advanced Scientific Computing Research; the DOE Computational Science Graduate Fellowship; and Sandia’s Laboratory Directed Research and Development program.

Optimization algorithms help industry perform tasks like coordinating trucking routes or managing financial assets. These problems are generally difficult to work out, Magann said, and as the number of variables increases, finding good solutions becomes harder.

One of the potential long-term solutions to solving complex optimization problems is to use quantum computers, an emerging technology that experts believe will be able to find answers to some problems much faster than supercomputers.

But building quantum computing technology is only one of the challenges.

“There’s also this other question of: Here’s a quantum computer — how do I actually program this thing? How do I use it?” Magann said.

Better solutions needed for large-scale applications

Researchers around the world are actively developing algorithms for large-scale optimizations on future technologies, with the hope that these programs could help industries manage limited resources more effectively and pivot operations more quickly in the face of rapid changes to the labor market, supplies of raw materials or other logistics.

Mohan Sarovar, the principal investigator on the project, said, “It’s very difficult to come up with quantum algorithms. One of the big reasons for this, apart from quantum computing being very unintuitive, is that we have very few general frameworks for developing quantum algorithms.”