  Quantum Computing (QC) promises computational speedup over classic computing
for solving complex problems. However, noise exists in current and near-term
quantum computers. Quantum software testing (for gaining confidence in quantum
software's correctness) is inevitably impacted by noise, to the extent that 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 testing quantum software on real quantum
computers or noisy simulators. To this end, 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 quantum 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 (QVM), with
nine real-world quantum programs and 1000 artificial quantum programs. Results
show that QOIN can reduce the noise effect by more than $80\%$ on the majority
of noise models. For quantum software testing, we used an existing test oracle
and 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. This highlights QOIN's
effectiveness in learning noise patterns for noise-aware quantum software
testing.
