Robust Mutation Analysis of Quantum Programs Under Noise

2026-05-13

AI Summary

This study addresses the limitation of existing quantum program mutation analysis, which neglects hardware noise and thus struggles to distinguish equivalent mutants from genuine faults on real devices. By systematically evaluating 41 quantum programs under simulated noise profiles of multiple IBM quantum devices, this work reveals that noise substantially alters the behavioral distance between original programs and their mutants, with the effect depending more on algorithmic and circuit characteristics than on mutation type. To mitigate this, we propose a noise-aware mutation analysis framework that integrates density matrix–based and output distribution–based metrics with noise-tailored thresholding strategies. Experimental results demonstrate that the density matrix metric achieves a misclassification rate as low as 16.77%, while the output distribution metric attains 73.03% accuracy and 74.89% F1 score in practical settings, with noise-specific thresholds significantly outperforming generic ones.

Abstract

Mutation analysis has long been used in classical software testing and has recently been adopted for assessing the robustness of quantum software testing techniques. However, existing studies assume ideal, noiseless execution, overlooking the impact of quantum hardware noise. In this paper, we present an empirical study of noise-aware mutation analysis for quantum programs. We analyze how noise affects mutant detection using 41 quantum programs, executed on noiseless and noisy simulators emulating three IBM devices with different noise profiles. We compare several distance metrics and thresholding strategies to evaluate mutant detection under realistic noise. Our results show that noise significantly alters the behavioral distance between programs and mutants, making equivalent mutants harder to distinguish from real faults. Density-matrix metrics achieve the best discrimination, with misclassification rates up to 16.77%, but are not accessible on real hardware. Among practical alternatives, output-distribution metrics reach up to 73.03% accuracy and 74.89% F1-score. Noise-specific thresholds further improve detection compared to noiseless thresholds. We also find that noise effects correlate more with algorithm and circuit characteristics than with mutation types. Overall, our results highlight the need to adapt mutation analysis, and more generally quantum program comparison, to the noise profiles of target quantum devices.

Problem

• mutation analysis
• quantum programs
• noise
• quantum software testing
• equivalent mutants

Innovation

• noise-aware mutation analysis
• quantum program testing
• behavioral distance metrics
• quantum hardware noise
• output-distribution metrics