With the growing synergy between deep learning and quantum computing, Quantum Neural Networks (QNNs) have emerged as a promising paradigm by leveraging quantum parallelism and entanglement. However, testing QNNs remains underexplored due to their complex quantum dynamics and limited interpretability. Developing a mutation testing technique for QNNs is promising while requires addressing stochastic factors, including the inherent randomness of mutation operators and quantum measurements. To tackle these challenges, we propose QuanForge, a mutation testing framework specifically designed for QNNs. We first introduce statistical mutation killing to provide a more reliable criterion. QuanForge incorporates nine post-training mutation operators at both gate and parameter levels, capable of simulating various potential errors in quantum circuits. Finally, a mutant generation algorithm is formalized that systematically produces effective mutants, thereby enabling a robust and reliable mutation analysis. Through extensive experiments on benchmark datasets and QNN architectures, we show that QuanForge can effectively distinguish different test suites and localize vulnerable circuit regions, providing insights for data enhancement and structural assessment of QNNs. We also analyze the generation capabilities of different operators and evaluate performance under simulated noisy conditions to assess the practical feasibility of QuanForge for future quantum devices.
With the growing synergy between deep learning and quantum computing, Quantum Neural Networks (QNNs) have emerged as a promising paradigm by leveraging quantum parallelism and entanglement. However, testing QNNs remains underexplored due to their complex quantum dynamics and limited interpretability. Developing a mutation testing technique for QNNs is promising while requires addressing stochastic factors, including the inherent randomness of mutation operators and quantum measurements. To tackle these challenges, we propose QuanForge, a mutation testing framework specifically designed for QNNs. We first introduce statistical mutation killing to provide a more reliable criterion. QuanForge incorporates nine post-training mutation operators at both gate and parameter levels, capable of simulating various potential errors in quantum circuits. Finally, a mutant generation algorithm is formalized that systematically produces effective mutants, thereby enabling a robust and reliable mutation analysis. Through extensive experiments on benchmark datasets and QNN architectures, we show that QuanForge can effectively distinguish different test suites and localize vulnerable circuit regions, providing insights for data enhancement and structural assessment of QNNs. We also analyze the generation capabilities of different operators and evaluate performance under simulated noisy conditions to assess the practical feasibility of QuanForge for future quantum devices.
Quantum machine learning integrates the strengths of quantum computing and machine learning, enabling models to learn complex features using fewer parameters than their classical counterparts. Due to the increasing complexity of quantum machine learning models, it is necessary to verify that the implementation of these models satisfy the design specification and be free of bugs and faults. Mutation testing is a promising avenue to identify faulty quantum circuits that do not meet design specifications or contain defects by intentionally inserting faults into the quantum circuit. It is necessary to define mutation operations to inject faults into quantum circuits to ensure that a test suite is robust enough to evaluate an implementation against its design specification. In this paper, we extend mutation testing to quantum machine learning applications, primarily quantum neural network models. Specifically, this paper makes two important contributions. We define new mutation operations for efficient fault insertion compared to state-of-the-art approaches. We also present a directed mutation generation technique to reduce redundant mutant circuits. Extensive experimental evaluation demonstrates that our approach generates a more diverse and representative set of mutants, effectively addressing faults that traditional techniques fail to expose.
Quantum Neural Networks (QNNs) have achieved initial success in various tasks by integrating quantum computing and neural networks. However, growing concerns about their reliability and robustness highlight the need for systematic testing. Unfortunately, current testing methods for QNNs remain underdeveloped, with limited practical utility and insufficient empirical evaluation. As an initial effort, we design a set of superposition-targeted coverage criteria to evaluate QNN state exploration embedded in test suites. To characterize the effectiveness, scalability, and robustness of the criteria, we conduct a comprehensive empirical study using benchmark datasets and QNN architectures. We first evaluate their sensitivity to input diversity under multiple data settings, and analyze their correlation with the number of injected faults. We then assess their scalability to increasing circuit scales. The robustness is further studied under practical quantum constraints including insufficient measurement and quantum noise. The results demonstrate the effectiveness of quantifying test adequacy and the potential applicability to larger-scale circuits and realistic quantum execution, while also revealing some limitations. Finally, we provide insights and recommendations for future QNN testing.
Abstract As a new research area, quantum software testing lacks systematic testing benchmarks to assess testing techniques’ effectiveness. Recently, some open-source benchmarks and mutation analysis tools have emerged. However, there is insufficient evidence on how various quantum circuit characteristics (e.g., circuit depth, number of quantum gates), algorithms (e.g., Quantum Approximate Optimization Algorithm), and mutation characteristics (e.g., mutation operators) affect the detection of mutants in quantum circuits. Studying such relations is important to systematically design faulty benchmarks with varied attributes (e.g., the difficulty in detecting a seeded fault) to facilitate assessing the cost-effectiveness of quantum software testing techniques efficiently. To this end, we present a large-scale empirical evaluation with more than 700K faulty benchmarks (quantum circuits) generated by mutating 382 real-world quantum circuits. Based on the results, we provide valuable insights for researchers to define systematic quantum mutation analysis techniques. We also provide a tool to recommend mutants to users based on chosen characteristics (e.g., a quantum algorithm type) and the required difficulty of detecting mutants. Finally, we also provide faulty benchmarks that can already be used to assess the cost-effectiveness of quantum software testing techniques.
Quantum software testing has attracted interest in recent years, prompting the development of various techniques to automate the testing of quantum software. These techniques generate test cases that must be assessed for their effectiveness in detecting faults. Such an assessment requires benchmarks of faulty programs. However, there is a lack of benchmarks containing faults. In this data showcase, we propose QMutBench, a dataset that contains over 700,000 quantum circuit mutants representing different faults. The dataset is accessible via an online interface with selection criteria, such as the original quantum circuit(s) from which mutants are generated, the desired survival rate of the selected mutants, and other mutation characteristics (e.g., the type of faulty quantum gate). QMutBench provides quantum software developers and testers with an accessible online dataset to obtain benchmarks of mutants necessary to assess either the quality of the test cases generated by their testing technique or to compare different testing techniques. It also enables the development of new mutation-guided quantum software testing techniques.
Quantum mutation analysis is emerging as an essential technique for evaluating test suites due to the limited availability of real faulty quantum programs. However, existing quantum mutation analysis tools use fixed gate-based mutations, resulting in easy-to-detect mutants, which reduces their effectiveness in assessing the quality of test suites. We propose QUMUG, a search-based approach for generating challenging mutants by utilising parameterisable quantum gates. QUMUG employs search algorithms to optimise mutation parameters and find non-equivalent mutants passing a given test suite. In our evaluation over 30 quantum programs, QUMUG produced mutants that are three times more challenging than the mutants generated by existing tools. Among the four evaluated search algorithms, the genetic algorithm was the most effective, generating an average of 494 undetected mutants per program with a 99.67% success rate and 94.3% non-equivalent ratio. The generated mutants demonstrated their effectiveness by requiring the addition of five times more test cases to the test suite than the mutants generated by existing tools. We also analysed the behaviour of higher order mutants in quantum circuits, and showed that while first order mutations are more effective for enhancing the test suite, higher order mutants highlight the need for new unique test cases.
As quantum computing is still in its infancy, there is an inherent lack of knowledge and technology to test a quantum program properly. In the classical realm, mutation testing has been successfully used to evaluate how well a program's test suite detects seeded faults (i.e., mutants). In this paper, building on the definition of syntactically equivalent quantum operations, we propose a novel set of mutation operators to generate mutants based on qubit measurements and quantum gates. To ease the adoption of quantum mutation testing, we further propose QMUTPY, an extension of the well-known and fully automated open-source mutation tool MUTPY. To evaluate QMUTPY's performance, we conducted a case study on 24 real quantum programs written in IBM's QISKIT library. Furthermore, we show how better test suite coverage and improvements to test assertions can increase the test suites' mutation score and quality. QMUTPY has proven to be an effective quantum mutation tool, providing insight into the current state of quantum tests and how to improve them.