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IEEE Transactions on Quantum Engineering

A Dynamic Testing Strategy With Incremental Learning Model for Quantum Programs

Lin Huang, Hanyu Pei, Yuechen Li, Beibei Yin, Kai-Yuan Cai

Published Oct 31, 2025• 1 citation
Publisher

Abstract

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 non-deterministic failure patterns associated with input quantum bits (qubits), which, if recognized, could potentially lead to more efficient fault detection. Therefore, in this paper, 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.

DOI
10.1109/tqe.2025.3626745

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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 non-deterministic failure patterns associated with input quantum bits (qubits), which, if recognized, could potentially lead to more efficient fault detection. Therefore, in this paper, 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.
Quantum Computing (QC) leverages quantum mechanics to manipulate quantum information, holding greater potential than classical computing. To fully exploit QC’s potential, it is crucial to ensure the reliability and quality of quantum programs. Research on quantum program testing is still at its early stage, in which some distinctive features of quantum programs, e.g., superposition and entanglement, may be overlooked, and the fault detection capability and testing effectiveness are rather limited. Besides, the input space of quantum programs may exponentially grow when the number of qubits increases, posing great challenges to testing quantum programs. It is imperative to develop a proper testing strategy to effectively select the potential failure-causing test cases and detect faults faster. In this paper, test cases with both basis states and superposition ones are considered and generated to cover more input space. A hybrid distance measurement method based on quantum fidelity and Hamming distance is presented for measuring the similarity among quantum test cases. Furthermore, a Dynamic Random Testing strategy based on Hybrid distance metrics (DRT-H) for quantum programs is proposed, which combines the hybrid distance metrics and the feedback mechanism of the classical Dynamic Random Testing (DRT) strategy to adjust the testing profile and guide the test case selection. Experimental studies demonstrate that the proposed DRT-H strategy outperforms the baseline testing strategies in most cases.
The capability of Quantum Computing (QC) in solving complex problems has been increasingly recognized. However, similar to classical computing, to fully exploit QC's potential, it is important to ensure the correctness of quantum programs. Doing so via software testing is, however, very challenging because of QC's inherent properties: superposition and entanglement. Towards the direction of ensuring the correctness of quantum programs, we propose an approach called QuCAT (QUantum CombinAtorial Testing) for systematic and automated testing of quantum programs by benefiting from combinatorial testing, which has been proven to be cost-effective in testing classical programs. QuCAT supports two combinatorial test suite generation scenarios, i.e., generating combinatorial test suites of a given strength, and incrementally generating and executing combinatorial test suites of increasing strength until a fault is found. The approach employs two types of test oracles to assess test results. We performed an empirical study with 18 faulty versions of quantum programs to evaluate QuCAT with strengths of two, three, and four in the two test generation scenarios. We compare the cost-effectiveness of combinatorial testing of various strengths and random testing (taken as baseline approach). Results show that combinatorial testing always performs better than random testing with the same cost and finds faults more quickly (in terms of required number of test cases). In addition, in most cases, combinatorial testing with a higher strength outperforms the lower strength in terms of effectiveness.
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.
Testing and debugging quantum software pose significant challenges due to the inherent complexities of quantum mechanics, such as superposition and entanglement. One challenge is indeterminacy, a fundamental characteristic of quantum systems, which increases the likelihood of flaky tests in quantum programs. To the best of our knowledge, there is a lack of comprehensive studies on quantum flakiness in the existing literature. In this paper, we present a novel machine learning platform that leverages multiple machine learning models to automatically detect flaky tests in quantum programs. Our evaluation shows that the extreme gradient boosting and decision tree-based models outperform other models (i.e., random forest, k-nearest neighbors, and support vector machine), achieving the highest F1 score and Matthews Correlation Coefficient in a balanced dataset and an imbalanced dataset, respectively. Furthermore, we expand the currently limited dataset for researchers interested in quantum flaky tests. In the future, we plan to explore the development of unsupervised learning techniques to detect and classify quantum flaky tests more effectively. These advancements aim to improve the reliability and robustness of quantum software testing.
Testing and debugging quantum software pose significant challenges due to the inherent complexities of quantum mechanics, such as superposition and entanglement. One challenge is indeterminacy, a fundamental characteristic of quantum systems, which increases the likelihood of flaky tests in quantum programs. To the best of our knowledge, there is a lack of comprehensive studies on quantum flakiness in the existing literature. In this paper, we present a novel machine learning platform that leverages multiple machine learning models to automatically detect flaky tests in quantum programs. Our evaluation shows that the extreme gradient boosting and decision tree-based models outperform other models (i.e., random forest, k-nearest neighbors, and support vector machine), achieving the highest F1 score and Matthews Correlation Coefficient in a balanced dataset and an imbalanced dataset, respectively. Furthermore, we expand the currently limited dataset for researchers interested in quantum flaky tests. In the future, we plan to explore the development of unsupervised learning techniques to detect and classify quantum flaky tests more effectively. These advancements aim to improve the reliability and robustness of quantum software testing.
This paper introduces a novel quantum machine learning framework designed for dynamic data distribution learning and pattern recognition. Traditional machine learning models often struggle with adapting to evolving data distributions, necessitating retraining or complex optimization strategies. This framework leverages quantum computing's inherent dynamic adaptability to achieve rapid learning and improved model accuracy. A key component of this design is the incorporation of quantum entanglement to enhance learning efficiency and model precision. We propose a method utilizing quantum algorithms to effectively capture and model complex data patterns, offering a potential paradigm shift in machine learning capabilities, particularly within dynamic environments. This work explores the potential benefits of this approach and presents preliminary results demonstrating its effectiveness in a specific benchmark dataset.