Triangle Quantum Computing Seminar Series: Quantum Multi-objective Optimization

Current noisy quantum computers typically tackle optimization problems by first mapping the problem to an Ising Hamiltonian and then sampling candidate solutions using the approximate quantum optimization algorithm. I will discuss advances in quantum multi-objective optimization (MOO) recently published in Nature Computation Science. MOO problems are challenging classically since one must find a collection of solutions known as the Pareto front. Here, quantum computers can help to quickly generate good solutions that explore the Pareto front. Dr. Daniel J. Egger is a Senior Research Scientist working at IBM Quantum, IBM Research Europe - Zurich. His research focuses on quantum algorithms and the tooling required to solved complex problems on quantum computers. He also works on the practical applications of quantum algorithms in finance, optimization, and natural sciences. Dr. Egger joined IBM in 2016. From 2014 to 2016 he worked in the asset management industry as a risk manager. He earned a PhD in theoretical physics in 2014 for his work on quantum simulations and optimal control of quantum computers based on superconducting qubits. --- Co-hosted by the Duke Quantum Center, the NC State Quantum Initiative, and the UNC Kenan-Flagler's Rethinc. Labs.