A Dynamic Testing Strategy With Incremental Learning Model for Quantum Programs

Quantum computing has emerged as an innovative computational paradigm with great potential in various domains. As quantum computing advances, the development of high-quality quantum programs has become crucial, making it essential to ensure their reliability. Software testing plays a vital role in achieving the reliability and quality of software systems. Various testing strategies and tools have been proposed for traditional programs; however, research on testing methodologies for quantum programs is still in the early stages. Traditional testing techniques, while effective for classical programs, struggle to address the unique challenges posed by quantum programs, including inherent characteristics of quantum systems (such as superposition and entanglement), and the exponentially expanding input space as the number of qubits increases. Moreover, traditional testing strategies typically do not account for the hidden and nondeterministic failure patterns associated with input quantum bits (qubits), which, if recognized, could potentially lead to more efficient fault detection. Therefore, in this article, we introduce a novel approach—Quantum Dynamic Testing with Incremental Learning (QDT-IL)—which aims to effectively capture and adapt to the failure patterns in quantum programs. QDT-IL employs an incremental learning model to learn from executed test cases and continuously updates its predictions on the failure tendencies for new test cases. By utilizing distance-based diversity metrics, QDT-IL strategically increases the variety of test cases, enabling targeted exploration of failure-prone regions in the input space. This combination of adaptive learning and diverse test case selection noticeably enhances the effectiveness and efficiency of quantum program testing. Experimental studies show that QDT-IL outperforms the baseline strategies, providing a more effective testing process for quantum programs.