Quantum computing is emerging as a powerful tool for solving problems that push the limits of classical methods. A new project led by industrial and systems engineering professors Tamás Terlaky and Luis F. Zuluaga will explore how quantum and classical computing can work together to tackle large-scale optimization challenges in process systems engineering.
The collaboration is supported by a National Science Foundation grant and includes partners from Lafayette College, the University of Southern California, and Purdue University. The team will focus on hybrid algorithms—approaches that combine classical and quantum devices—to address computationally demanding problems in industries such as chemical production and energy systems.
“For over a decade, research in quantum optimization has concentrated on combinatorial problems that, while important, are fairly narrow in scope,” says Terlaky. “Our goal is to take the first steps toward showing how quantum devices can support decision-making across a wider range of industrial applications.”
Zuluaga’s expertise in polynomial optimization and energy systems complements Terlaky’s background in conic optimization and quantum computing. Their collaborators bring additional strengths in quantum linear algebra, applied computing, and chemical engineering optimization, creating a team positioned to investigate how quantum methods might expand into new areas of engineering practice.
If successful, the research could provide a foundation for future advances in applying quantum technologies to real-world systems—bridging the gap between theory and practical implementation.