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arXiv:2604.15870

QMutBench: A Dataset of Quantum Circuit Mutants

Eñaut Mendiluze Usandizaga, Thomas Laurent, Paolo Arcaini, Shaukat Ali

Published Apr 17, 2026• 0 citations
arXivHTMLPDF

Abstract

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.

DOI
10.48550/arxiv.2604.15870

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•May 30, 2023•1 citation
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.
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 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.
Given that quantum software testing is a new area of research, there is a lack of benchmark programs and bugs repositories to assess the effectiveness of testing techniques. To this end, quantum mutation analysis focuses on systematically generating faulty versions of Quantum Programs (QPs), called mutants, using mutation operators. Such mutants can be used as benchmarks to assess the quality of test cases in a test suite. Thus, we present Muskit - a quantum mutation analysis tool for QPs coded in IBM's Qiskit language. Muskit defines mutation operators on gates of QPs and selection criteria to reduce the number of mutants to generate. Moreover, it allows for the execution of test cases on mutants and generation of results for test analyses. Muskit is provided as command line interface, GUI, and web application. We validated Muskit by using it to generate and execute mutants for four QPs. Muskit code: https://github.com/Simula-COMPLEX/muskitWeb app: https://qiskitmutantcreatorsrl.pythonanywhere.com/YouTube Video: EbPHJOK_AEA Artifact Available: https://doi.org/10.5281/zenodo.5288917
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.
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.
Abstract Software testing is an important activity to ensure the software's quality. Mutation testing is a fault-based technique for checking the effectiveness of test cases for the verification and validation of software. Testing quantum programs is challenging due to the involvement of quantum mechanics concepts (such as superposition and entanglement ) and the probabilistic nature of the outcome. In this paper, We will propose the modified mutation testing approach for quantum program. The proposed approach will help to reduce the cost, time, and effort of performing the mutation testing. Moreover, we also propose a method for determining the equivalent mutant and developing a tool to check whether the mutated and original quantum program behave semantically equivalent or differently.