Quantum Approximate Multi-Objective Optimization
Fuente:
arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866912669369368576 |
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| author | Kotil, Ayse Pelofske, Elijah Riedmüller, Stephanie Egger, Daniel J. Eidenbenz, Stephan Koch, Thorsten Woerner, Stefan |
| author_facet | Kotil, Ayse Pelofske, Elijah Riedmüller, Stephanie Egger, Daniel J. Eidenbenz, Stephan Koch, Thorsten Woerner, Stefan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_22797 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum Approximate Multi-Objective Optimization Kotil, Ayse Pelofske, Elijah Riedmüller, Stephanie Egger, Daniel J. Eidenbenz, Stephan Koch, Thorsten Woerner, Stefan Quantum Physics 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. |
| title | Quantum Approximate Multi-Objective Optimization |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2503.22797 |