TY - JOUR A1 - Kotil, Ayse A1 - Pelofske, Elijah A1 - Riedmüller, Stephanie A1 - Egger, Daniel J. A1 - Eidenbenz, Stephan A1 - Koch, Thorsten A1 - Woerner, Stefan T1 - Quantum Approximate Multi-Objective Optimization T2 - Nature Computational Science N2 - The goal of multi-objective optimization is to understand optimal trade-offs between competing objective functions by finding the Pareto front, i.e., the set of all Pareto optimal solutions, where no objective can be improved without degrading another one. Multi-objective optimization can be challenging classically, even if the corresponding single-objective optimization problems are efficiently solvable. Thus, multi-objective optimization represents a compelling problem class to analyze with quantum computers. In this work, we use low-depth Quantum Approximate Optimization Algorithm to approximate the optimal Pareto front of certain multi-objective weighted maximum cut problems. We demonstrate its performance on an IBM Quantum computer, as well as with Matrix Product State numerical simulation, and show its potential to outperform classical approaches. Y1 - 2025 UR - https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/10003 SP - 1168 EP - 1177 ER -