Testability—the degree to which faults can be revealed and diagnosed—has long been fundamental in software engineering but remains largely unexplored for quantum software. This work presents the first formalisation of testability for quantum programs, adapting classical concepts to quantum computation. We introduce Quantum Squeeziness, an information-theoretic metric inspired by classical squeeziness, to quantify how faults are masked and prevented from propagating to observable outcomes. We treat it as an inverse measure of testability: higher squeeziness implies stronger fault masking and reduced detectability.
Testability—the degree to which faults can be revealed and diagnosed—has long been fundamental in software engineering but remains largely unexplored for quantum software. This work presents the first formalisation of testability for quantum programs, adapting classical concepts to quantum computation. We introduce Quantum Squeeziness, an information-theoretic metric inspired by classical squeeziness, to quantify how faults are masked and prevented from propagating to observable outcomes. We treat it as an inverse measure of testability: higher squeeziness implies stronger fault masking and reduced detectability.
Testability—the degree to which faults can be revealed and diagnosed—has long been fundamental in software engineering but remains largely unexplored for quantum software. This work presents the first formalisation of testability for quantum programs, adapting classical concepts to quantum computation. We introduce Quantum Squeeziness, an information-theoretic metric inspired by classical squeeziness, to quantify how faults are masked and prevented from propagating to observable outcomes. We treat it as an inverse measure of testability: higher squeeziness implies stronger fault masking and reduced detectability. We propose an efficient method to compute Quantum Squeeziness and validate it through large-scale quantum mutation analysis. Across more than 50k mutants, we observe a strong negative correlation between squeeziness and mutation score, showing that higher squeeziness predicts lower testability. We further introduce a technique that constrains where and how perturbations are injected, reducing cost while preserving accuracy. The computation is 15% faster than a standard error-propagation baseline, while the optimised variant, FastSqueeziness, reduces mean runtime from 3200 s to 189 s (16.9×speedup) with 95% accuracy. By establishing a rigorous foundation of testability for quantum software, this work opens new directions at the intersection of software engineering, quantum information theory, and testing. Beyond testing and debugging, it also supports applications in quantum error mitigation and correction, enabling principled assessment and improvement of program robustness.
Abstract Testability—the degree to which faults can be revealed and diagnosed—has long been fundamental in Software Engineering but remains unexplored for quantum software. This work presents the first formalisation of testability for quantum programs, adapting classical concepts to the unique properties of quantum computation. We introduce Quantum Squeeziness, an information-theoretic metric inspired by the classical squeeziness measure, to quantify how effectively faults propagate to observable failures. We provide an efficient computational method for calculating Quantum Squeeziness and validate it through large-scale quantum mutation testing. Across more than 50k mutants executed over several months, we observe a strong negative correlation between squeeziness and mutation score, demonstrating that higher squeeziness reliably predicts reduced fault detectability. Performance results show that the standard squeeziness computation is already 15% faster than baseline methods, while our optimised variant, FastSqueeziness, reduces mean runtime from 3200 s to 189 s, achieving a 16.9× speedup with 95% accuracy. By establishing a rigorous notion of testability for quantum software, this study opens a new line of research at the intersection of software engineering, quantum information theory, and testing. Beyond its immediate implications for testing and debugging, the proposed framework lays the foundation for future applications in quantum error mitigation and error correction, providing a principled means to assess and improve the robustness of quantum programs. Artifact Contents The archive Quantum_Squeeziness_AE_Kit.zip contains all materials required for ASE 2026 Artifact Evaluation. Specifically, it includes: Complete source code Benchmark quantum circuits Precomputed experimental results Docker environment Dependency specifications Documentation Replication scripts for RQ1–RQ3 A copy of the accepted paper (ase26-paper151.pdf) The artifact is self-contained and can be executed without access to external services. Precomputed results are provided for experiments whose full recomputation would require weeks or months of execution time, while all scripts necessary to regenerate those results are also included. Consequently, Quantum_Squeeziness_AE_Kit.zip constitutes the complete artifact-evaluation package and contains everything required by reviewers to assess the Available, Functional, and Reusable badges, including software, data, documentation, execution environments, and reproduction instructions. Replication Steps Setup Create a virtual environment and install the packages listed in requirements.txt. RQ1 Run: python3 testabilityBenchmark_RQ1/run_benchmark.py In line 885, you can modify folder_path to select the desired benchmark folder and run larger experiments. RQ2 Compute Quantum Squeeziness Run: python3 definingSqueeziness_RQ2/squeeziness.py to produce the Squeeziness results for a given QASM folder. Line 931: choose the origin folder. Line 932: choose the target folder. Compute FastSqueeziness Run: python3 definingSqueeziness_RQ2/fast_squeeziness.py to produce the FastSqueeziness results for a given QASM folder. Line 937: choose the origin folder. Line 938: choose the target folder. Note: Running either script on the complete mutant dataset requires several months of computation. For testing purposes, we recommend using smaller subsets such as the MQT or chosen QASM folders. Analysis Run: python3 definingSqueeziness_RQ2/analyse_dqcSqueeziness.py python3 definingSqueeziness_RQ2/evaluate_fastSqueeziness.py python3 definingSqueeziness_RQ2/statistical_analysisSqueezinessR2.py to reproduce the analysis results. RQ3 Prepare Mutation Results Unzip Results_mutants_chosen.zip and place the extracted folder inside: validatingSqueeziness_RQ3/ Alternatively, the results can be reproduced manually using: python3 validatingSqueeziness_RQ3/mutantsGeneratorBasic.py python3 validatingSqueeziness_RQ3/executeCircuit.py These scripts originate from Muskit (see the reference in line 1 of the source file). Reproducing the complete dataset manually may require several weeks of execution. Reproduce FastSqueeziness Validation Results Generate and organise result folders: python3 results/combine_folder.py Select mutants within the desired Squeeziness range: python3 validatingSqueeziness_RQ3/select_mutants.py \ --origin_csv definingSqueeziness_RQ2/correlation_analysis_chosen/Fastsqueeziness_metrics_summary.csv \ --mutant_info_folder validatingSqueeziness_RQ3/resultsFastSqueezinessMutantsChosen100Iterations \ --mutants_results_root validatingSqueeziness_RQ3/Results_mutants_chosen \ --output_folder validatingSqueeziness_RQ3/selectedMutantsChosen_FastSqueeziness \ --qasm_original_root qasm_circuits/chosen \ --qasm_mutant_root validatingSqueeziness_RQ3/Mutants_chosen Execute mutation testing: python3 validatingSqueeziness_RQ3/analyse.py \ --oracle validatingSqueeziness_RQ3/chosen_oracle \ --mutants validatingSqueeziness_RQ3/selectedMutantsChosen_FastSqueeziness/ \ --output validatingSqueeziness_RQ3/Test_killingMutants_FastSqueeziness_Chosen Produce the final correlation plots: python3 validatingSqueeziness_RQ3/correlation_squeeziness_kill_ratio.py Running these scripts recreates all necessary intermediate directories and the complete set of results. The configuration can be modified to reproduce alternative experiments (e.g., MQT datasets or standard Squeeziness results). Performance Comparison Run: python3 validatingSqueeziness_RQ3/compare_squeezinessFast.py to analyse the performance of FastSqueeziness compared with Squeeziness. Additional Materials Additional experiments, exploratory analyses, and investigations are available in the: .experimentations/ folder.
Quantum software testing concentrates on testing quantum programs to discover quantum faults in the programs cost-effectively. Given the foundations in quantum mechanics, the way quantum computations are performed is significantly different than classical computing. Therefore, quantum software testing also differs from classical software testing. There has been quite an interest in building quantum software testing techniques since 2019 in the software engineering (SE) community. Thus, we aim to introduce quantum software testing to the SE community. In particular, we will present the basic foundations of quantum computing and quantum programming as circuits, followed by the current state of the art on quantum software testing. Next, we will present some basic quantum software testing techniques and finally give the research directions that deserve attention from the SE community.
Quantum software is often described using circuit-level metrics, including the number of qubits, the number of gates, circuit depth, the number of two-qubit gates, and the number of measurement operations. These metrics are useful for estimating program size and execution cost, but they do not directly describe whether and to what extent qubits become structurally related through possible entanglement during program execution. This paper argues that entanglement dependence provides a basis for measuring a quantum-specific form of software complexity. The proposed metrics capture possible entanglement-related structural dependence, not physical entanglement observed in a particular execution. Based on this notion, we define a focused set of six metrics that characterize the amount, density, local connectivity, global scope, program span, and modular crossing of entanglement-related dependencies. We also analyze these metrics using Weyuker’s properties for evaluating software complexity measures. The paper presents the metrics as a measurement framework and research direction for empirical quantum software engineering. It discusses how future studies can validate the metrics and examine their usefulness for testing, debugging, maintenance, and program comprehension of quantum software.
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.
As quantum computing transitions from theoretical concepts to commercially available quantum processing units, ensuring the quality, reliability, and correctness of quantum software systems has become paramount. The unique challenges of quantum software testing have necessitated the adaptation of classical software engineering techniques to the quantum domain. One such technique is fuzz testing, or fuzzing, an automated software testing technique that generates large volumes of inputs to identify bugs and unexpected behaviors. In this work we provide a comprehensive examination of how fuzz testing has been successfully applied to quantum computing. We analyze the application of fuzzing across the quantum ecosystem, from testing quantum programs to verifying quantum software stacks, quantum compilers, and quantum simulators. This survey establishes the foundation for understanding how fuzz testing can ensure the reliability and correctness of quantum software systems and outlines promising future research directions.