Quantum Hamiltonian Descent Algorithm for Nonlinear Optimization and Applications in Energy Systems
Abstract: Nonlinear optimization is a highly active research area due to its broad applications in engineering and science. However, classical algorithms often struggle with local minima, limiting their effectiveness in nonconvex problems.
In this talk, we will explore how quantum dynamics can be leveraged to design new quantum optimization algorithms. We will begin with an introduction to quantum computing and quantum optimization, followed by a discussion of the theoretical properties of the Quantum Hamiltonian Descent (QHD) algorithm, including its global convergence and convergence rate in nonlinear and nonconvex optimization settings. Finally, we will introduce the open-source implementation of the QHD algorithm and showcase its applications in energy systems.
Bio: Lei Fan is an assistant professor in the Department of Engineering Technology and holds a joint appointment with the Department of Electrical and Computer Engineering at the University of Houston. He earned his Ph.D. in Operations Research from the Department of Industrial and Systems Engineering at the University of Florida.
See all upcoming talks at https://www.anl.gov/mcs/lans-seminars