Quantum program testing method improves accuracy despite noise
Noise-Resilient Testing of Quantum Programs Based on Output Dominance
Software Engineering
Summary
Quantum programs can be tricky to test because errors from the hardware make it hard to tell if a program is broken or if the output is just noisy. The authors developed a new way called Qomino that can check if a quantum program is working correctly even when there is noise, without needing long noise measurements or complicated models. They showed that their method works more accurately and faster than other existing methods on many tested programs and simulated quantum machines.
What this means in practice
- •For quantum software developers: Check correctness of quantum programs on noisy hardware without costly noise calibration steps.
- •For quantum hardware teams: Assess real-world program faults versus device-induced noise to improve quantum device testing workflows.
Authors
Yuechen Li, Yige Yang, Tao Yue
Abstract
Quantum software testing (QST) is a central quality-assurance activity for checking whether quantum programs conform to their specifications. In the noisy intermediate-scale quantum (NISQ) era, gate, decoherence, and measurement errors distort observed outputs, making it difficult to distinguish program defects from noise. The existing noise-aware QST literature typically mitigates or models noise before applying a test oracle. However, these strategies can incur substantial cost and depend on calibration data, model training, or backend-specific information, limiting their applicability. We present Qomino, a dominance-guided QST approach for judging tests from noisy samples without a separate noise-estimation or mitigation phase. The approach combines decomposition-aware inverse construction, comparison of the expected outcome with the strongest observed competitor, and bounded retries for inconclusive decisions. We evaluate Qomino on 30 controlled buggy variants from six Qiskit programs and six simulated noisy backends, comparing it with six baselines spanning statistical, quantum-specific, and learning-based methods. Qomino achieves 85.65% accuracy over all six programs and 99.95% in the restricted two-program evaluation, significantly outperforming the corresponding baselines. Across the evaluated programs and backends under the stated settings, Qomino is faster than the compared quantum-specific and learning-based methods. Ablation and retry analyses support the contributions of its main components.