The Quantum Optimization Benchmarking Library

Thorsten Koch; David E. Bernal; Yuehua Chen; G. Cortiana; Daniel J. Egger; Raoul Heese; Narendra N. Hegade; Alejandro Gomez Cadavid; Yuxin Huang; Toshinari Itoko; Thomas Kleinert; Pedro Maciel Xavier; Naeimeh Mohseni; J. A. Montañez-Barrera; Koji Nakano; Giacomo Nannicini; Corey O’Meara; Justin Pauckert; Manuel Proissl; Anurag Ramesh; Maximilian Schicker; Noriaki Shimada; Mitsuharu Takeori; Víctor Valls; David Van Bulck; Stefan Woerner; Christa Zoufal

doi:10.1038/s43588-026-00991-1

Key Points

The aim is to introduce a library that allows for fair and reproducible benchmarks of quantum heuristics.
Development of the Quantum Optimization Benchmarking Library
Inclusion of ten difficult combinatorial optimization classes
Provision of baseline results for tracking progress.
Establishment of a framework for assessing quantum heuristics
Facilitation of reproducibility in quantum optimization research
Providing baseline metrics to gauge quantum advantage.

Implication

Resource presents a library for benchmarking quantum heuristics in combinatorial optimization, highlighting quantum advantage.

Abstract

This Resource presents the Quantum Optimization Benchmarking Library, which enables fair, reproducible benchmarks of quantum heuristics for ten difficult combinatorial optimization classes with baseline results to track progress towards quantum advantage.

Discussion

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Cite This Study

Koch et al. (2026) studied this question.

synapsesocial.com/papers/6a3e15e7030ad1a9b308fdf5 https://doi.org/10.1038/s43588-026-00991-1

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The Quantum Optimization Benchmarking Library

Key Points

Implication

Abstract

Discussion

Cite This Study

The Quantum Optimization Benchmarking Library

Key Points

Implication

Abstract

Discussion

Cite This Study

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