Speaker
Mohammadhossein Mohammadisiahroudi
(Department of Mathematics and Statistics, University of Maryland Baltimore County)
Description
Quantum linear algebra has emerged as a promising framework for accelerating the solution of fundamental computational problems, including systems of linear equations—a core subroutine in many scientific and engineering tasks. These problems arise prominently in optimization algorithms. In this talk, we discuss the opportunities and challenges associated with integrating quantum linear algebra techniques into modern optimization methods. We focus on two key applications: quantum interior point methods for conic optimization and a quantum adjoint method for PDE-constrained optimization. Together, these illustrate how quantum algorithms may reshape large-scale optimization by offering new pathways toward improved scalability and performance.
Author
Mohammadhossein Mohammadisiahroudi
(Department of Mathematics and Statistics, University of Maryland Baltimore County)