Quantum Concolic Testing

This paper presents the first concolic testing framework explicitly designed for quantum programs. The framework introduces quantum constraint generation methods for quantum control statements that quantify quantum states and offers a symbolization method for quantum variables. Based on this framework, we generate path constraints for each concrete execution path of a quantum program. These constraints guide the exploration of new paths, with a quantum constraint solver determining outcomes to create novel input samples, thereby enhancing branch coverage. Our framework has been implemented in Python and integrated with Qiskit for practical evaluation. Experimental results show that our concolic testing framework improves branch coverage, generates high-quality quantum input samples, and detects bugs, demonstrating its effectiveness and efficiency in quantum programming and bug detection. Regarding branch coverage, our framework achieves more than 74.27% on quantum programs with under 5 qubits.

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Publication Details

Journal
Proceedings of the ACM on software engineering.
Published
2025-06-22
DOI
https://doi.org/10.1145/3728926
Citations
2
Primary Topic
Quantum Computing Algorithms and Architecture
Type
article
Field-Weighted Citation Impact
4.96
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article

Quantum Concolic Testing

Jianjun Zhao, Xiaoyu Guo, Fuyuan Zhang, Shangzhou Xia
2 citations
Proceedings of the ACM on software engineering.
Quantum Computing Algorithms and Architecture
4.96
article

Quantum Concolic Testing

Jianjun Zhao, Xiaoyu Guo, Fuyuan Zhang, Shangzhou Xia
article en
2 citations

Abstract

This paper presents the first concolic testing framework explicitly designed for quantum programs. The framework introduces quantum constraint generation methods for quantum control statements that quantify quantum states and offers a symbolization method for quantum variables. Based on this framework, we generate path constraints for each concrete execution path of a quantum program. These constraints guide the exploration of new paths, with a quantum constraint solver determining outcomes to create novel input samples, thereby enhancing branch coverage. Our framework has been implemented in Python and integrated with Qiskit for practical evaluation. Experimental results show that our concolic testing framework improves branch coverage, generates high-quality quantum input samples, and detects bugs, demonstrating its effectiveness and efficiency in quantum programming and bug detection. Regarding branch coverage, our framework achieves more than 74.27% on quantum programs with under 5 qubits.

Proceedings of the ACM on software engineering.Vol. 2(ISSTA)
Kyushu University (JP), The University of Tokyo (JP)
Openalex Percentile: Top 5%
Quantum Computing Algorithms and Architecture
4.96
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Quantum Concolic Testing — Jianjun Zhao, Xiaoyu Guo, et al. · Proceedings of the ACM on software engineering. (2025) | TGRS Research Map | TGRS