Enhancing Variational Quantum Algorithms for Multicriteria Optimization

• URL: http://arxiv.org/abs/2506.22159v1
• Date: Fri, 27 Jun 2025 12:15:22 GMT
• Title: Enhancing Variational Quantum Algorithms for Multicriteria Optimization
• Authors: Ivica Turkalj, Tom Ewen, Pascal Halffmann, Janik Maciejewski, Michael Trebing, Zakaria Abdelmoiz Dahi,
• Abstract summary: We introduce two key contributions to the field of variational quantum algorithms.<n>First, we reformulate the parameter optimization task of VQAs as a multicriteria problem.<n>Second, we propose a method that augments the hypervolume-based cost function with coverage-oriented indicators.
• Score: 0.19791587637442667
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents methodological improvements to variational quantum algorithms (VQAs) for solving multicriteria optimization problems. We introduce two key contributions. First, we reformulate the parameter optimization task of VQAs as a multicriteria problem, enabling the direct use of classical algorithms from various multicriteria metaheuristics. This hybrid framework outperforms the corresponding single-criteria VQAs in both average and worst-case performance across diverse benchmark problems. Second, we propose a method that augments the hypervolume-based cost function with coverage-oriented indicators, allowing explicit control over the diversity of the resulting Pareto front approximations. Experimental results show that our method can improve coverage by up to 40\% with minimal loss in hypervolume. Our findings highlight the potential of combining quantum variational methods with classical population-based search to advance practical quantum optimization.

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