Quantum Computing (QC) promises computational speedup over classic computing. However, noise exists in nearterm 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 realworld 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.