12 | LEHIGH ALUMNI BULLETIN MAPPING THE MIND Haley Bennett MS’26, a master’s student in cognitive psychology in the College of Arts and Sciences, employs eye-tracking and electroencephalography (EEG) methods to investigate how the brain prioritizes information amid distraction. Eye-tracking reveals where participants look, showing what captures their focus. EEG measures electrical activity in the brain, offering insight into shifts in spatial attention. Her findings aim to reveal how spatial attention may strengthen working memory for items even after they’re no longer visible. “If we can learn how to strengthen focus and reduce the impact of distractions, we can improve learning, productivity and overall well-being,” Bennett says. “I hope my research not only advances knowledge in the lab but also provides tools people can use in their everyday lives.”—Abby Ryan Quantum computing is an emerging approach to computation that uses the principles of quantum mechanics to solve certain problems far more efficiently than classical computers— potentially reshaping what kinds of problems machines can solve at all. The idea of the quantum computer was first proposed in the 1980s and since then, it has evolved into one of the most ambitious technological races of our time. Luis F. Zuluaga, professor in the industrial and systems engineering department in the P.C. Rossin College of Engineering and Applied science, is helping advance the research that is shaping this new frontier through his work on quantum and quantum-inspired optimization methods. Unlike traditional computers, which store information in bits (ones or zeros states), quantum computers use qubits, which can exist in a superposition of both states at once. This ability to represent many possibilities simultaneously transforms the scale of what’s possible. Zuluaga’s research focuses on how these capabilities can be applied to hard optimization problems that classical computers struggle to solve efficiently. As part of Lehigh’s Quantum Computing and Optimization Lab (QCOL), he studies how to translate complex, real-world questions, like how to make a system more efficient or a decision more reliable, into forms that quantum computers and advanced classical machines can tackle. Zuluaga is part of a cross-disciplinary research team that was initially funded by a DARPA ONISQ grant and is now supported by the National Science Foundation (NSF). Their work spans several major challenges: developing quantum algorithms for optimization and decision-making, modeling and mitigating noise in near-term quantum devices, and designing problem frameworks that can take advantage of emerging quantum architectures. Institutions like IBM and Google are expecting quantum technologies to account for a significant share of their business within the next two decades. Hospitals have already begun investing in quantum systems for medical modeling and pharmaceutical research. The excitement, Zuluaga says, stems from possibility: quantum computing could solve problems we can barely articulate today. For Zuluaga, the field is exciting because it forces researchers to rethink the fundamentals of computation itself: what becomes possible when machines begin to operate according to the laws of nature at the quantum level. Redefining the Limits of Problem-Solving
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