Perspectives of Quantum Computing in Chemical Engineering
Speaker
David E. Bernal Neira
Assistant Professor, Davidson School of Chemical Engineering
Purdue University
Abstract
Quantum technologies have attracted considerable interest, and among them, quantum computing has been attracting significant public attention recently. This interest is driven by advancements in hardware, software, and algorithms required for its successful use, as well as the promise of accelerating computational tasks compared to classical computing. This perspective talk reviews quantum computing, how this computational approach solves problems, and three fields that it can most impact in chemical engineering: computational chemistry, optimization, and machine learning. Here, we present a series of chemical engineering applications, their developments, potential improvements over classical computing, and the challenges that quantum computing faces in each field. The first part of this talk aims to provide a clear picture of the challenges and potential benefits that quantum technology may offer to chemical engineering, and to invite our colleagues to join us in adopting and developing quantum computing. The second part presents recent work by the Systems Engineering via Classical and Quantum Optimization for Industrial Applications (SECQUOIA) research group to bring these computational tools closer to all chemical engineers. This talk corresponds to the invited publication in Perspectives on Quantum Computing for Chemical Engineering in the AIChE Journal (https://doi.org/10.1002/aic.17651) and to receiving the best talk award at the 2022 Quantum Computing Applications in Chemical and Biochemical Engineering Workshop.
Bio
David E. Bernal Neira is an Assistant Professor in the Davidson School of Chemical Engineering at Purdue University, where he leads the SECQUOIA group (Systems Engineering via Classical and Quantum Optimization for Industrial Applications). Holding undergraduate degrees in Physics and Chemical Engineering and a Ph.D. from Carnegie Mellon University, he bridges first-principles modeling with rigorous mathematical optimization.
His work focuses on Systems Engineering, specifically the co-design of models, algorithms, and control strategies for sustainable energy and critical infrastructure. He is a core developer of widely used open-source optimization software (including Pyomo and MindtPy). In addition to his classical optimization work, he serves as the Co-Chair of the INFORMS Quantum Computing Committee and previously worked as a Research Scientist at NASA’s Quantum AI Laboratory, where he benchmarked emerging hardware for engineering applications. He collaborates broadly with national laboratories and industry to translate theoretical advances into deployable decision-support tools.