Towards Robust Benchmarking of Quantum Optimization Algorithms
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929341456187392 |
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| author | Bucher, David Kraus, Nico Blenninger, Jonas Lachner, Michael Stein, Jonas Linnhoff-Popien, Claudia |
| author_facet | Bucher, David Kraus, Nico Blenninger, Jonas Lachner, Michael Stein, Jonas Linnhoff-Popien, Claudia |
| contents | Benchmarking the performance of quantum optimization algorithms is crucial for identifying utility for industry-relevant use cases. Benchmarking processes vary between optimization applications and depend on user-specified goals. The heuristic nature of quantum algorithms poses challenges, especially when comparing to classical counterparts. A key problem in existing benchmarking frameworks is the lack of equal effort in optimizing for the best quantum and, respectively, classical approaches. This paper presents a comprehensive set of guidelines comprising universal steps towards fair benchmarks. We discuss (1) application-specific algorithm choice, ensuring every solver is provided with the most fitting mathematical formulation of a problem; (2) the selection of benchmark data, including hard instances and real-world samples; (3) the choice of a suitable holistic figure of merit, like time-to-solution or solution quality within time constraints; and (4) equitable hyperparameter training to eliminate bias towards a particular method. The proposed guidelines are tested across three benchmarking scenarios, utilizing the Max-Cut (MC) and Travelling Salesperson Problem (TSP). The benchmarks employ classical mathematical algorithms, such as Branch-and-Cut (BNC) solvers, classical heuristics, Quantum Annealing (QA), and the Quantum Approximate Optimization Algorithm (QAOA). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_07624 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Robust Benchmarking of Quantum Optimization Algorithms Bucher, David Kraus, Nico Blenninger, Jonas Lachner, Michael Stein, Jonas Linnhoff-Popien, Claudia Quantum Physics Emerging Technologies Benchmarking the performance of quantum optimization algorithms is crucial for identifying utility for industry-relevant use cases. Benchmarking processes vary between optimization applications and depend on user-specified goals. The heuristic nature of quantum algorithms poses challenges, especially when comparing to classical counterparts. A key problem in existing benchmarking frameworks is the lack of equal effort in optimizing for the best quantum and, respectively, classical approaches. This paper presents a comprehensive set of guidelines comprising universal steps towards fair benchmarks. We discuss (1) application-specific algorithm choice, ensuring every solver is provided with the most fitting mathematical formulation of a problem; (2) the selection of benchmark data, including hard instances and real-world samples; (3) the choice of a suitable holistic figure of merit, like time-to-solution or solution quality within time constraints; and (4) equitable hyperparameter training to eliminate bias towards a particular method. The proposed guidelines are tested across three benchmarking scenarios, utilizing the Max-Cut (MC) and Travelling Salesperson Problem (TSP). The benchmarks employ classical mathematical algorithms, such as Branch-and-Cut (BNC) solvers, classical heuristics, Quantum Annealing (QA), and the Quantum Approximate Optimization Algorithm (QAOA). |
| title | Towards Robust Benchmarking of Quantum Optimization Algorithms |
| topic | Quantum Physics Emerging Technologies |
| url | https://arxiv.org/abs/2405.07624 |