Quantum Approximate Multi-Objective Optimization

• URL: http://arxiv.org/abs/2503.22797v1
• Date: Fri, 28 Mar 2025 18:00:25 GMT
• Title: Quantum Approximate Multi-Objective Optimization
• Authors: Ayse Kotil, Elijah Pelofske, Stephanie Riedmüller, Daniel J. Egger, Stephan Eidenbenz, Thorsten Koch, Stefan Woerner,
• Abstract summary: Multi-objective optimization represents a compelling problem class to analyze with quantum computers.<n>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.<n>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.
• Score: 1.2564343689544843
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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