From Ideal to Noisy: Adapting Property-Based Testing for Real-World Noisy Quantum Computers
Gabriel Pontolillo, Asmar Muqeet, Shaukat Ali +1 more
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.
IEEE International Conference on Quantum Computing and Engineering (QCE)•Aug 30, 2025•0 citations