NovaQ: Improving Quantum Program Testing through Diversity-Guided Test Case Generation
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912571799371776 |
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| author | Jin, Tiancheng Xia, Shangzhou Zhao, Jianjun |
| author_facet | Jin, Tiancheng Xia, Shangzhou Zhao, Jianjun |
| contents | Quantum programs are designed to run on quantum computers, leveraging quantum circuits to solve problems that are intractable for classical machines. As quantum computing advances, ensuring the reliability of quantum programs has become increasingly important. This paper introduces NovaQ, a diversity-guided testing framework for quantum programs. NovaQ combines a distribution-based test case generator with a novelty-driven evaluation module. The generator produces diverse quantum state inputs by mutating circuit parameters, while the evaluator quantifies behavioral novelty based on internal circuit state metrics, including magnitude, phase, and entanglement. By selecting inputs that map to infrequently covered regions in the metric space, NovaQ effectively explores under-tested program behaviors. We evaluate NovaQ on quantum programs of varying sizes and complexities. Experimental results show that NovaQ consistently achieves higher test input diversity and detects more bugs than existing baseline approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_04763 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | NovaQ: Improving Quantum Program Testing through Diversity-Guided Test Case Generation Jin, Tiancheng Xia, Shangzhou Zhao, Jianjun Software Engineering Quantum programs are designed to run on quantum computers, leveraging quantum circuits to solve problems that are intractable for classical machines. As quantum computing advances, ensuring the reliability of quantum programs has become increasingly important. This paper introduces NovaQ, a diversity-guided testing framework for quantum programs. NovaQ combines a distribution-based test case generator with a novelty-driven evaluation module. The generator produces diverse quantum state inputs by mutating circuit parameters, while the evaluator quantifies behavioral novelty based on internal circuit state metrics, including magnitude, phase, and entanglement. By selecting inputs that map to infrequently covered regions in the metric space, NovaQ effectively explores under-tested program behaviors. We evaluate NovaQ on quantum programs of varying sizes and complexities. Experimental results show that NovaQ consistently achieves higher test input diversity and detects more bugs than existing baseline approaches. |
| title | NovaQ: Improving Quantum Program Testing through Diversity-Guided Test Case Generation |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2509.04763 |