Quantum computing has the potential to revolutionize many areas of technology, including supply chain security. The current supply chain systems rely heavily on cryptography to secure data and ensure the authenticity of products. However, the cryptographic algorithms that are currently in use could be broken by a quantum computer, which would render the security of the supply chain vulnerable.
New quantum computing research is bringing science closer to overcoming supply-chain challenges and restoring global security during future periods of unrest. The Russian-Ukrainian conflict and the COVID-19 pandemic have demonstrated the vulnerability of global supply chains. International events can cause manufacturing to be disrupted, shipping to be delayed, panic buying to occur, and energy costs to skyrocket.
Sandia National Laboratories’ new quantum computing research is bringing 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 that limits global trade agility,” said Alicia Magann, a Truman Fellow at Sandia. She has led the development of a new method for designing programs on quantum computers, which she and her colleagues believe will be especially useful for solving these types of massive optimization problems in the future as quantum technology matures.
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.
It is extremely difficult to develop quantum algorithms. Apart from the fact that quantum computing is very unintuitive, one of the major reasons for this is that there are very few general frameworks for developing quantum algorithms.
Mohan Sarovar
Optimization algorithms aid industry in tasks such as coordinating truck routes and managing financial assets. According to Magann, these problems are generally difficult to solve, and as the number of variables increases, finding good solutions becomes more difficult.
One potential long-term solution to complex optimization problems is to use quantum computers, a new technology that experts believe will be able to solve some problems much faster than supercomputers. However, developing quantum computing technology is only one of many 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 all over the world are hard at work developing algorithms for large-scale optimizations on future technologies in the hopes that these programs will help industries manage limited resources more effectively and pivot operations more quickly in the face of rapid changes in the labor market, raw material supplies, or other logistics.
Mohan Sarovar, the project’s principal investigator, stated, “It is extremely difficult to develop quantum algorithms. Apart from the fact that quantum computing is very unintuitive, one of the major reasons for this is that there are very few general frameworks for developing quantum algorithms.”
A leading idea for programming quantum optimization algorithms has involved coupling quantum computers and conventional ones to solve a problem together, called the variational approach. The conventional computer performs an optimization of control settings that dictate the behavior of the quantum computer.
One disadvantage of this approach is that its impact is limited by the conventional computer’s ability to solve optimization problems with a large number of parameters. Sandia scientist Kenneth Rudinger, who also worked on the project, believes the variational approach may be impractical when quantum computers reach their full potential.
“We have good reason to believe that the size of the kinds of problems you’d want to solve is too large for the variational approach; at that scale, it becomes essentially impossible for the conventional computer to find good settings for the quantum device,” he said.
New framework to solve intricate problems
The Sandia team was successful in significantly reducing the role of traditional computing. The classical computer does no optimization with the new framework, known as FALQON (Feedback-based Algorithm for Quantum Optimization). It only requires the computational power of a calculator, allowing the quantum computer to do all of the heavy lifting and theoretically allowing it to work on much more complicated problems, such as how to efficiently reroute a shipping fleet when a major port closes unexpectedly.
In this context, a framework is a structure for writing an algorithm. Sandia’s central idea is for a quantum computer to constantly adapt its structure as it performs a calculation. Layers of quantum computing gates, the building blocks of quantum algorithms, are determined by measurements of the output of previous layers through a feedback process.
“After I run the first layer of the algorithm, I measure the qubits and get some information from them,” Magann said. “I feed that information back to my algorithm and use that to define the second layer. I then run the second layer, measure the qubits again, feed that information back for the third layer, and so on and so forth.”
Sarovar said, “It defines another class of quantum algorithms that operate through feedback.”
Until quantum computers become more powerful, the framework will remain primarily a theoretical tool that can only be tested on problems that classical computers can already solve. However, the team believes that the framework has great potential for developing useful algorithms for future medium-to-large-scale quantum computers. They’re curious to see if it can aid in the development of quantum computing algorithms for solving problems in chemistry, physics, and machine learning.
