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arXiv:2306.16992 · IEEE Transactions on Software Engineering

Mitigating Noise in Quantum Software Testing Using Machine Learning

Asmar Muqeet, Tao Yue, Shaukat Ali, Paolo Arcaini

Published Sep 18, 2024• 27 citations
arXivHTMLPDF

Abstract

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$\mathit{QOIN}$) to alleviate the noise effect on test results of quantum programs.$\mathit{QOIN}$employs 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$\mathit{QOIN}$on 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$\mathit{QOIN}$can reduce the noise effect by more than$80\%$on most noise models. We used an existing test oracle to evaluate$\mathit{QOIN}$'s effectiveness in quantum software testing. The results showed that$\mathit{QOIN}$attained 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,$\mathit{QOIN}$achieved scores of$93\%$,$79\%$, and$86\%$for precision, recall, and F1-score respectively. This highlights$\mathit{QOIN}$'s effectiveness in learning noise patterns for noise-aware quantum software testing.

DOI
10.1109/tse.2024.3462974

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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\mathit{QOIN}QOIN) to alleviate the noise effect on test results of quantum programs.QOIN\mathit{QOIN}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 evaluatedQOIN\mathit{QOIN}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 thatQOIN\mathit{QOIN}QOINcan reduce the noise effect by more than80%80\%80%on most noise models. We used an existing test oracle to evaluateQOIN\mathit{QOIN}QOIN's effectiveness in quantum software testing. The results showed thatQOIN\mathit{QOIN}QOINattained scores of99%99\%99%,75%75\%75%, and86%86\%86%for precision, recall, and F1-score, respectively, for the test oracle across six real-world programs. For artificial programs,QOIN\mathit{QOIN}QOINachieved scores of93%93\%93%,79%79\%79%, and86%86\%86%for precision, recall, and F1-score respectively. This highlightsQOIN\mathit{QOIN}QOIN's effectiveness in learning noise patterns for noise-aware quantum software testing.
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.
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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.
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.
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