Quantum software testing is essential for the quality assurance of quantum programs. However, existing techniques face many challenges, such as constructing test oracles and distinguishing genuine faults from noise-induced errors. Propertybased testing is a promising solution for dealing with the test oracle issue, which verifies general properties rather than requiring inputs and outputs. However, its effectiveness in noisy quantum environments has not been studied. To this end, we evaluate the feasibility of applying property-based testing in noisy quantum computers by integrating it with a state-of-the-art machine learning-based noise mitigation method called QOIN. Our results show that on average, QOIN mitigates noise for individual circuits, though it may introduce outliers. Crucially, this does not guarantee property preservation: we show that some properties align better with QOIN, while others remain closer under unmitigated noise. To address this, we apply a hybrid approach that selectively applies QOIN during property-based testing. We show that this hybrid approach improves alignment with the ideal execution and significantly reduces false positives in assertion outcomes across most executed mutants. This provides a solid foundation for applying property-based testing to noisy quantum systems.
Quantum software testing is essential for the quality assurance of quantum programs. However, existing techniques face many challenges, such as constructing test oracles and distinguishing genuine faults from noise-induced errors. Propertybased testing is a promising solution for dealing with the test oracle issue, which verifies general properties rather than requiring inputs and outputs. However, its effectiveness in noisy quantum environments has not been studied. To this end, we evaluate the feasibility of applying property-based testing in noisy quantum computers by integrating it with a state-of-the-art machine learning-based noise mitigation method called QOIN. Our results show that on average, QOIN mitigates noise for individual circuits, though it may introduce outliers. Crucially, this does not guarantee property preservation: we show that some properties align better with QOIN, while others remain closer under unmitigated noise. To address this, we apply a hybrid approach that selectively applies QOIN during property-based testing. We show that this hybrid approach improves alignment with the ideal execution and significantly reduces false positives in assertion outcomes across most executed mutants. This provides a solid foundation for applying property-based testing to noisy quantum systems.
Quantum Computing (QC) promises computational speedup over classic computing. However, noise exists in near-term quantum computers. Quantum software testing (for gaining confidence in quantum software's correctness) is inevitably impacted by noise, i.e., it is impossible to know if a test case failed due to noise or real faults. Existing testing techniques test quantum programs without considering noise, i.e., by executing tests on ideal quantum computer simulators. Consequently, they are not directly applicable to test quantum software on real quantum computers or noisy simulators. Thus, we propose a noise-aware approach (namedQOIN) to alleviate the noise effect on test results of quantum programs.QOINemploys machine learning techniques (e.g., transfer learning) to learn the noise effect of a quantum computer and filter it from a program's outputs. Such filtered outputs are then used as the input to perform test case assessments (determining the passing or failing of a test case execution against a test oracle). We evaluatedQOINon IBM's 23 noise models, Google's two available noise models, and Rigetti's Quantum Virtual Machine, with six real-world and 800 artificial programs. We also generated faulty versions of these programs to check if a failing test case execution can be determined under noise. Results show thatQOINcan reduce the noise effect by more than80%on most noise models. We used an existing test oracle to evaluateQOIN's effectiveness in quantum software testing. The results showed thatQOINattained scores of99%,75%, and86%for precision, recall, and F1-score, respectively, for the test oracle across six real-world programs. For artificial programs,QOINachieved scores of93%,79%, and86%for precision, recall, and F1-score respectively. This highlightsQOIN's effectiveness in learning noise patterns for noise-aware quantum software testing.
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.
Quantum computers have the potential to outperform classical computers for some complex computational problems. However, current quantum computers (e.g., from IBM and Google) have inherent noise that results in errors in the outputs of quantum software executing on the quantum computers, affecting the reliability of quantum software development. The industry is increasingly interested in machine learning (ML)-based error mitigation techniques, given their scalability and practicality. However, existing ML-based techniques have limitations, such as only targeting specific noise types or specific quantum circuits. This paper proposes a practical ML-based approach, called Q-LEAR, with a novel feature set, to mitigate noise errors in quantum software outputs. We evaluated Q-LEAR on eight quantum computers and their corresponding noisy simulators, all from IBM, and compared Q-LEAR with a state-of-the-art ML-based approach taken as baseline. Results show that, compared to the baseline, Q-LEAR achieved a 25% average improvement in error mitigation on both real quantum computers and simulators. We also discuss the implications and practicality of Q-LEAR, which, we believe, is valuable for practitioners.
Property-based testing has been previously proposed for quantum programs in Q# with QSharpCheck; however, this implementation was limited in functionality, lacked extensibility, and was evaluated on a narrow range of programs using a single property. To address these limitations, we propose QuCheck, an improved property-based testing framework for Qiskit. By leveraging Qiskit and the broader Python ecosystem, QuCheck facilitates property construction, introduces flexible input generators and assertions, and supports expressive preconditions. We assessed its effectiveness through mutation analysis on five quantum programs (2-10 qubits), varying the number of properties, inputs, and measurement shots to assess their impact on fault detection and demonstrate the effectiveness of property-based testing across a range of conditions. Results show a strong positive correlation between the mutation score (a measure of fault detection) and number of properties evaluated, with a moderate negative correlation between the false positive rate and number of measurement shots. The most thorough test configurations achieved a mean mutation score of 0.90 averaged across all five algorithms, with a false positive rate between 0 and 0.06. QuCheck identified 47.8% more faults than QSharpCheck, with execution time reduced by 67.4%. These findings highlight the viability of property-based testing for verifying quantum systems.
Machine learning has been extensively applied for classical software testing activities such as test generation, minimization, and prioritization. Along the same lines, there has been interest in applying quantum machine learning to classical software testing. For example, Quantum Extreme Learning Machines (QELMs) were recently applied for testing classical software of industrial elevators. However, studies on QELMs, whether in software testing or other areas, used ideal simulators that fail to account for the noise in current quantum computers. While ideal simulations offer insight into QELM's theoretical capabilities, they do not enable studying their performance on current noisy quantum computers. To this end, we study how quantum noise affects QELM in three industrial classical software testing case studies, providing insights into QELMs' robustness to noise for software testing applications. Such insights assess QELMs potential as a viable solution for software testing problems in today's noisy quantum computing. Our results show that QELMs are significantly affected by quantum noise, with a performance drop of 250% in regression and 50% in classification software testing tasks. Quantum noise also increases uncertainty in QELM models, producing a saturation effect where larger qubit counts make the models increasingly random and unreliable. While error mitigation techniques can enhance noise resilience, achieving an average 3% performance drop in classification, their effectiveness varies by context. For classification tasks, QLEAR performs well, whereas Zero Noise Extrapolation is more effective for regression and smaller qubit counts. However, no single mitigation approach consistently reduces uncertainty across tasks or scales reliably as the number of qubits increases, highlighting the need for QELM-tailored strategies.
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.