Triangle Quantum Computing Seminar Series: The Quest for Quantum Advantage in Combinatorial Optimization

Abstract: Showing quantum advantage in combinatorial optimization requires going beyond isolated algorithmic comparisons and benchmarking full workflows under realistic runtime conditions. In this talk, I discuss a hardware-aware workflow for portfolio selection with cardinality constraints over a 250-asset universe drawn from the S&P 500 on trapped-ion processors. I also present end-to-end benchmarking of a hybrid quantum-classical solver for higher-order binary optimization on 156-qubit superconducting processors against strong multicore CPU and GPU baselines. The results show that quantum methods can already be competitive on selected problems, while also making clear where the best classical solvers still have the edge. Bio: Narendra Hegade is a physicist and Fellow at Kipu Quantum, where he develops hardware-aware quantum algorithms for quantum optimization, quantum simulation, and quantum AI. His research focuses on digitized-counterdiabatic and digital-analog quantum computing, especially methods tailored to current quantum hardware. He is particularly interested in translating ideas from quantum many-body physics and control into algorithms for industrial applications. -- Co-hosted by the Duke Quantum Center, the NC State Quantum Initiative, and the UNC Kenan-Flagler's Rethinc. Labs.