Quantum Computing (QC) promises computational speedup over classic computing
for solving some 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 QC hardware 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
with nine real-world quantum programs and 1000 artificial quantum 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%. To check QOIN's effectiveness for quantum
software testing, we used an existing test oracle for quantum software testing.
The results showed that the F1-score of the test oracle was improved on average
by 82% for six real-world programs and by 75% for 800 artificial
programs, demonstrating that QOIN can effectively learn noise patterns and
enable noise-aware quantum software testing