Methods and platforms for developing and testing quantum software

Linsey Kitt

Quantum computing is a field that has drawn a lot of attention over the years as it has shown great potential to solve new problems and has seen rapid development. To meet the demands of this rapid development, software engineering researchers have been studying methods to address the unique challenges presented by the probabilistic nature of quantum computing in its software platforms. One important aspect of this is understanding how to test and validate quantum software platforms when the expected outputs when running a program are not always known. Recent research using metamorphic testing was able to detect 13 faults in the Qiskit quantum computing platform; however, this is a rapidly evolving platform. Since their work, Qiskit has already advanced ahead several versions, which brings up more questions regarding research for quantum software platforms: do these tools researchers create hold up across versions of these frequently-updated platforms, and how much work does it take to maintain and extend these tools? In this thesis, we study the ease of regression testing in the context of this quantum computing platform. We update to the latest version of Qiskit, extend two existing metamorphic transformations on a metamorphic testing platform for Qiskit, add six new metamorphic transformations, and perform a set of metamorphic testing runs to analyze how our changes perform. In doing so, we were able to discover five additional new faults on the Qiskit platform; however, we also uncovered several challenges, which we synthesize for the reader. We also offer discussion on where the future work of metamorphic testing on quantum software platforms may lead based on our challenges and successes during this process.

Avner Bensoussan, Elena Chachkarova, Karine Even-Mendoza +3 more

Contemporary quantum computers are inherently noisy, posing significant challenges for the development and testing of quantum software. Simplified or outdated noise assumptions can lead to incorrect assessments of program correctness, obscure debugging, and hinder cross-platform portability, creating a critical quantum software development gap. Providing accurate, practical noise characterisation is challenging as traditional reconstruction methods scale exponentially and rapidly become outdated. In this vision paper, we address this gap via a novel classical shadow tomography-based pipeline, SIMSHADOW, enabling efficient, continuously updatable noise fingerprinting from empirical observations, suitable for integration into quantum software development workflows, including testing and validation. We prototyped the pipeline to investigate fingerprints' ability to capture structured, interpretable noise and cross-platform discrepancies affecting quantum programs' behaviour to support realistic testing and debugging in future tools. Our evaluation with Qiskit and Cirq under widely used hardware-informed profiles, IBM Boston and Quantinuum H2, shows fingerprints exhibit channel-specific structure and yield interpretable heatmaps. We observed systematic cross-platform discrepancies under matched noise configurations, quantified by large Frobenius distances at a fraction of full tomography cost. On 69 MQTBENCH programs, larger fingerprint differences correlate with output distributions divergences, highlighting threats for testing and cross-platform debugging tasks.

Yoann Marquer, Domenico Bianculli

Antonio García de la Barrera, Ignacio García‐Rodríguez de Guzmán, Macario Polo +1 more

Avner Bensoussan, Elena Chachkarova, Karine Even-Mendoza +3 more

Contemporary quantum computers are inherently noisy, posing significant challenges for the development and testing of quantum software. Simplified or outdated noise assumptions can lead to incorrect assessments of program correctness, obscure debugging, and hinder cross-platform portability, creating a critical quantum software development gap. Providing accurate, practical noise characterisation is challenging as traditional reconstruction methods scale exponentially and rapidly become outdated. In this vision paper, we address this gap via a novel classical shadow tomography-based pipeline, SIMSHADOW, enabling efficient, continuously updatable noise fingerprinting from empirical observations, suitable for integration into quantum software development workflows, including testing and validation. We prototyped the pipeline to investigate fingerprints' ability to capture structured, interpretable noise and cross-platform discrepancies affecting quantum programs' behaviour to support realistic testing and debugging in future tools. Our evaluation with Qiskit and Cirq under widely used hardware-informed profiles, IBM Boston and Quantinuum H2, shows fingerprints exhibit channel-specific structure and yield interpretable heatmaps. We observed systematic cross-platform discrepancies under matched noise configurations, quantified by large Frobenius distances at a fraction of full tomography cost. On 69 MQTBENCH programs, larger fingerprint differences correlate with output distributions divergences, highlighting threats for testing and cross-platform debugging tasks.

Macario Polo