Mir Mohammad Yousuf, Shabir Ahmad Sofi, Bisma Majid · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.25233 · cited by 0 works (OpenAlex) · open-access PDF
В то же время, несмотря на то, что тестирование квантового программного обеспечения (QST) быстро появилось в качестве активной области исследований, она остается неясной в какой степени существующие аналитические рамки на методологии тестирования, опубликованные в период между 2018 и 2025 годами в восьми парадигмах тестирования, явно взаимодействуют с шумовыми и аппаратными средами выполнения, в то время как в этой статье представлен систематический эмпирический анализ ландшафта шумосознания в исследованиях QST через детерминистскую, таксономически ориентированную аналитическую структуру, которая применяется к 61 статье, опубликованной в восьми парадигмах тестирования, в контрасте с тем, как вклад вносится с тем, как они оцениваются. Среди аналитических работ, 47 (77.0
Machine translation (Meta M2M100); the English original follows.
Noisy Intermediate-Scale Quantum (NISQ) hardware has transformed quantum computing into a practical experimental platform while introducing fundamental challenges associated with hardware noise, decoherence, and probabilistic execution. Although quantum software testing (QST) has rapidly emerged as an active research area, it remains unclear to what extent existing testing methodologies explicitly engage with noisy and hardware-constrained execution environments. This paper presents a systematic empirical landscape analysis of noise-awareness in QST research through a deterministic, taxonomy-guided analytical framework applied to 61 papers published between 2018 and 2025 across eight testing paradigms, contrasting how contributions are framed with how they are evaluated. Among the analyzed papers, 47 (77.0%) are noise-oblivious and only 14 (23.0%) explicitly engage with noise-related execution concerns, while only 11 (18.0%) report evaluation on physical hardware or a noisy simulator. Test generation, the largest paradigm, exhibits a noise-awareness rate of only 14.3%, and debugging and repair 18.2%; coverage-based, metamorphic, and differential testing contain no noise-aware contributions. Noise-aware methodologies emerged late and remain outnumbered in all analyzed years. Framing and evaluation are strongly associated (odds ratio 40.5, p < 0.001) yet diverge for individual papers: 5 of the 14 noise-aware papers evaluate only on ideal simulators. Inferential statistical tests are reported in only 16.4% of papers. The classification framework was validated against a blinded, human-coded reference sample, achieving almost perfect inter-rater agreement on testing-technique classification and substantial agreement on noise-awareness. The framework provides a reusable empirical basis for longitudinal analysis of hardware-conscious testing practice.
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Topics: Debugging, Computer science, Framing (construction), Probabilistic logic, Software, Statistical hypothesis testing
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