[en] As quantum computing continues to emerge, ensuring the quality of quantum programs has become increasingly critical. Quantum program testing has emerged as a prominent research area within the scope of quantum software engineering. While numerous approaches have been proposed to address quantum program quality assurance, our analysis reveals that most existing methods rely on measurement-based validation in practice. However, due to the inherently probabilistic nature of quantum programs, measurement-based validation methods face significant limitations. To investigate these limitations, we conducted an empirical study of recent research on quantum program testing, analyzing measurement-based validation methods in the literature. Our analysis categorizes existing measurement-based validation methods into two groups: distribution-level validation and output-valuelevel validation. We then compare measurement-based validation with statevector-based validation methods to evaluate their pros and cons. Our findings demonstrate that measurement-based validation is suitable for straightforward assessments, such as verifying the existence of specific output values, while statevectorbased validation proves more effective for complicated tasks such as assessing the program behaviors.
Disciplines :
Computer science
Author, co-author :
Ye, Jiaming; Southwest Jiaotong University, China
WU, Xiongfei ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SerVal
Xia, Shangzhou; Kyushu University, Japan
Zhang, Fuyuan; Zhejiang University, China
Zhao, Jianjun; Kyushu University, Japan
External co-authors :
yes
Language :
English
Title :
Is Measurement Enough? Rethinking Output Validation in Quantum Program Testing
Publication date :
2025
Event name :
40th IEEE/ACM International Conference on Automated Software Engineering
Event place :
Seoul, South Korea
Event date :
16-20 November 2025
Audience :
International
Main work title :
Proceedings of the 40th IEEE/ACM International Conference on Automated Software Engineering (ASE 2025)
Publisher :
IEEE Computer Society, Los Alamitos, CA, United States
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