Aggregated scores

List of quantum-specific methodologies

• Myriad-Q (2024) [1]: In a nutshell, the MYRIAD-Q methodology aggregates multiple quantum computing performance metrics into a single score based on decision-maker preferences. It uses Multi-Criteria Decision Analysis (MCDA) to normalize individual metrics according to subjective utility functions and combine them using a Choquet integral, which accounts for interactions between criteria. The resulting score reflects the decision-maker’s priorities.

• Quantum Application Score (QuAS) (2024) [2]: In a nutshell, the Quantum Application Score (QuAS) methodology combines multiple Key Performance Indicators (KPIs) into a single score to evaluate quantum computer performance on optimization problems. It analyzes the trade-offs between KPIs, such as solution quality and runtime, by approximating Pareto fronts and computing the areas under the resulting curves across different problem sizes.

• Metriq Score (2026) [3]: In a nutshell, the Metriq Score methodology aggregates results from multiple system-level and application-level quantum benchmarking protocols into a single performance score. Individual benchmark results are first combined across circuit or problem sizes, normalized against a reference quantum computer, and then aggregated using a weighted average.

References

1. [1]F. Barbaresco et al., “BACQ–Application-oriented Benchmarks for Quantum Computing,” arXiv preprint arXiv:2403.12205, 2024.
2. [2]K. J. Mesman, W. van der Schoot, M. Möller, and N. M. P. Neumann, “QuAS: Quantum Application Score for benchmarking the utility of quantum computers,” in 2024 IEEE International Conference on Quantum Computing and Engineering (QCE), IEEE, 2024, pp. 921–929.
3. [3]A. Cosentino et al., “Metriq: A Collaborative Platform for Benchmarking Quantum Computers,” arXiv preprint arXiv:2603.08680, 2026.