Lightning Talk Presenter

David Bernal

David Bernal

Purdue University

About

Assistant Professor in the Davidson School of Chemical Engineering at Purdue University. He specializes in applying mathematical and computer science tools to address problems relevant to science and engineering, for example, physics and chemical, process, and energy systems engineering. In particular, he works in nonlinear discrete optimization, where, besides applications, he has been working in theory, algorithms, and software. He has been involved in research and teaching related to these topics for over a decade, complemented with research in Quantum Computing.

He is currently a visiting research scientist at the NASA Quantum Artificial Intelligence Laboratory and the Research Institute of Advanced Computer Science from the Universities Space Research Association (USRA). He has co-authored several papers in indexed journals, a few software packages for optimization and quantum computing, given more than 50 presentations and seminars in the United States of America and internationally, co-taught more than five different classes, including one he co-designed (on the intersection of optimization, quantum computing, and machine learning), and have had the pleasure of collaborating with over a hundred different researchers in academia, national laboratories, government agencies, and industry.

Decomposition of problems: making problem easier for quantum computing

Quantum computers have the potential to efficiently solve challenging computational problems. For optimization, current devices can already implement a type of nonlinear and combinatorial problem. However, available quantum computers cannot yet address practical problems; they are limited to small sizes and do not handle constraints well. In this talk and tutorial, we present the modeling strategy of discrete nonlinear optimization known as Mixed-Integer Nonlinear Programming, explain some of the approaches that quantum computers use to solve Quadratic Unconstrained Binary Optimization (QUBO) problems, and share our contributions on addressing practical optimization problems using quantum computers. We highlight our work on reformulations of MINLP to QUBOs and hybrid classical-quantum algorithms to solve a subclass of MINLP considering constraints and global convergence via decomposition strategies. These strategies rely on breaking the problems down into QUBO subproblems that can be solved by quantum computers and classical routines that ensure robustness and state-of-the-art efficiency. Finally, we present recent work on learning with privacy guarantees.

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