Quantum computing is expected to introduce the next era of computing speed and power, and its software - quantum program is gaining increasing research interest in the software engineering community. A significant characteristic of quantum computing is the existence of noise. Unlike classical computers where the output of a program is usually deterministic, the execution of a quantum program may be affected by quantum noise. Such a difference may cause difficulties or misunderstandings for developers shifting from classical programming to quantum programming. To understand the impact of quantum noise on quantum programs and its implications for software developers, we conduct a series of studies with real-world quantum programs and quantum computing environments. Specifically, we first measure and analyze the noise in a real quantum computer by testing it with a basic quantum program. We find that a non-neglectable amount of quantum noise generally exists in real quantum computers. Then we investigate the robustness of quantum programs against different quantum noises by testing 18 real-world quantum programs and 50,000 randomly generated quantum circuits in simulated and real environments. We observe that quantum noise can significantly influence the correctness of quantum programs, and different quantum circuit structures show diverse sensitivity patterns under the same noise. Based on the observations, we build a machine learning model to predict the fidelity of a quantum program under certain quantum noise. The model achieves a small average fidelity prediction error, meaning the impact of noise can be precisely estimated statistically.
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.
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 (named
QOIN) 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 evaluated
QOINon 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 that
QOINcan reduce the noise effect by more than
80%on most noise models. We used an existing test oracle to evaluate
QOIN's effectiveness in quantum software testing. The results showed that
QOINattained scores of
99%,
75%, and
86%for precision, recall, and F1-score, respectively, for the test oracle across six real-world programs. For artificial programs,
QOINachieved scores of
93%,
79%, and
86%for precision, recall, and F1-score respectively. This highlights
QOIN's effectiveness in learning noise patterns for noise-aware quantum software testing.
Context: The main obstacle of quantum computing on its way to scalability is the noisy nature of current hardware. Various types of errors lead to incorrect computational results,and measures such as quantum error mitigation are needed to counteract these errors. Many error mitigation techniques require a thorough understanding of the noise and its impact on computations. Therefore, accurate noise models are indispensable to facilitate meaningful quantum computing in the near future. They also enable realistic simulations of quantum devices. Since these devices are currently limited and quantum computing is expensive, researchers typically rely on simulations for testing their algorithms.Objective: In this thesis, we aim to construct a realistic noise model for quantum computing and optimise its model parameters. The model construction should build on the underlying physical processes, and the number of parameters should scale well with the system size. We also address the evaluation and comparison of different noise models by benchmarks using quantum circuits specific to real-world applications. Finally, we aim to develop useful graphical representations of noise models based on the existing quantum circuit model.Method: We present a benchmarking framework for quantum computing noise models and evaluate it based on benchmarking quality attributes from the literature. Moreover, we construct a quantum computing noise model and develop a training procedure for its parameters. We analyse the parameters of the trained noise model and the impact ofdifferent types of errors on computations. We benchmark the noise model with the above approach and compare it to a noise model provided by IBM’s software development kit Qiskit. Finally, we develop an extension of the quantum circuit model to represent noise channels.Result: The benchmarks performed in this thesis show that our noise model predicts noisy hardware behaviour of IBM’s ibmq_manila quantum device equally well as the Qiskit model, if not better, based on quantum circuits commonly used for variational quantum algorithms. Within the trained noise model, readout error has the most detrimental impact on computations. Our benchmarking approach satisfies relevant quality criteria by choosing suitable quantum circuits and objective functions to compare model predictionsto hardware data. Moreover, the quantum circuit model can be extended meaningfully by noise channels.Conclusion: Our benchmarking approach is suitable for evaluating and comparing quantum computing noise models. In future work, volumetric benchmarks should be performed for various noise models and application contexts. The parameter optimisation based on learning with training circuits yields accurate noise models and shows the importance of mitigating measurement error. Different optimisation algorithms and quantum circuits should be explored, and more types of noise should be incorporated into the noise model.
While quantum computers provide exciting opportunities for information processing, they currently suffer from noise during computation that is not fully understood. Incomplete noise models have led to discrepancies between quantum program success rate (SR) estimates and actual machine outcomes. For example, the estimated probability of success (ESP) is the state-of-the-art metric used to gauge quantum program performance. The ESP suffers poor prediction since it fails to account for the unique combination of circuit structure, quantum state, and quantum computer properties specific to each program execution. Thus, an urgent need exists for a systematic approach that can elucidate various noise impacts and accurately and robustly predict quantum computer success rates, emphasizing application and device scaling. In this article, we propose quantum vulnerability analysis (QVA) to systematically quantify the error impact on quantum applications and address the gap between current success rate (SR) estimators and real quantum computer results. The QVA determines the cumulative quantum vulnerability (CQV) of the target quantum computation, which quantifies the quantum error impact based on the entire algorithm applied to the target quantum machine. By evaluating the CQV with well-known benchmarks on three 27-qubit quantum computers, the CQV success estimation outperforms the estimated probability of success state-of-the-art prediction technique by achieving on average six times less relative prediction error, with best cases at 30 times, for benchmarks with a real SR rate above 0.1%. Direct application of QVA has been provided that helps researchers choose a promising compiling strategy at compile time.
Quantum Computing demonstrates potential exponential speedup over classical computing in a plethora of tasks including chemistry simulation, linear algebra, and large integer factorization. Machine learning is one such popular application that benefits from this advantage of quantum computers to facilitate speedup. However, due to the inherent noise in quantum computers, machine learning algorithms encounter problems relating to fidelity and accuracy. Existing research has addressed these issues pertaining to the unreliable execution of machine learning models in noisy quantum computers. In this paper, we explore the effects of noise in quantum machine learning and demonstrate approaches to mitigate this issue.
ABSTRACT Executing quantum circuits (QCs) on noisy intermediate‐scale quantum (NISQ) devices faces significant challenges due to quantum noise that impacts computational reliability. Layout selection presents a promising approach to enhance execution quality by mapping logical qubits to high‐fidelity physical qubits. Nevertheless, traditional approaches struggle to find high‐fidelity layouts mainly because of the difficulties in modeling noise characteristics. In this work, a machine learning‐driven layout optimization framework is introduced. By developing a novel feature set that considers information of circuit structures, backend calibration data and related factors, the trained machine learning (ML) model can accurately predict the fidelity of QCs under different candidate layouts, facilitating selecting high‐fidelity layouts. By evaluating proposed framework on tianyan176‐2 quantum computer using 6486 random QCs and 10 real‐world quantum programs, the results provide three key insights: first, the random forest model demonstrates superior noise modeling capabilities compared to the traditional product model and other ML models; second, the proposed framework significantly improves QC execution fidelity compared with random layout selection and Mapomatic ; furthermore, the SHapley Additive exPlanations (SHAP) analysis is conducted, which reveals strong correlations between features likes liveness and depth with QC execution fidelity, thereby facilitating understanding the random forest model's decision‐making mechanism in layout optimization.