Mitigating Noise in Quantum Software Testing Using Machine Learning

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. 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 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 QOIN can 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 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, QOIN achieved 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.