Quantum Approximate Multi‑Objective Optimization and Other Paths to Quantum Advantage in Optimization

Abstract: In this talk, we discuss possible paths toward demonstrating quantum advantage in optimization, focusing primarily on quantum approximate multi-objective optimization. Multi-objective optimization seeks to understand optimal trade-offs between competing objectives by identifying sets of solutions that balance these trade-offs, a task that can be challenging for classical methods. This makes it a natural candidate for exploring potential benefits of quantum computing. We present a quantum algorithm for approximating such trade-offs in combinatorial optimization problems and illustrate its performance using both experiments on IBM quantum hardware and classical simulations. We conclude by discussing complementary paths toward quantum advantage in optimization, highlighting the role of systematic benchmarking efforts such as the Quantum Optimization Benchmarking Library (QOBLIB).
 

Bio: Dr. Stefan Woerner is a Principal Research Scientist, Global Technical Lead for Quantum Optimization, and Manager of the Applied Quantum Science group of IBM Quantum at IBM Research Europe - Zurich. He holds a Master of Science in Applied Mathematics and a Doctor of Sciences in Operations Management from ETH Zurich and is renowned for his work in quantum optimization, quantum finance, and quantum machine learning.