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As a new research area, quantum software testing lacks systematic testingbenchmarks to assess testing techniques' effectiveness. Recently, someopen-source benchmarks and mutation analysis tools have emerged. However, thereis insufficient evidence on how various quantum circuit characteristics (e.g.,circuit depth, number of quantum gates), algorithms (e.g., Quantum ApproximateOptimization Algorithm), and mutation characteristics (e.g., mutationoperators) affect the most mutant detection in quantum circuits. Studying suchrelations is important to systematically design faulty benchmarks with variedattributes (e.g., the difficulty in detecting a seeded fault) to facilitateassessing the cost-effectiveness of quantum software testing techniquesefficiently. To this end, we present a large-scale empirical evaluation withmore than 700K faulty benchmarks (quantum circuits) generated by mutating 382real-world quantum circuits. Based on the results, we provide valuable insightsfor researchers to define systematic quantum mutation analysis techniques. Wealso provide a tool to recommend mutants to users based on chosencharacteristics (e.g., a quantum algorithm type) and the required difficulty ofkilling mutants. Finally, we also provide faulty benchmarks that can already beused to assess the cost-effectiveness of quantum software testing techniques.

Which Quantum Circuit Mutants Shall Be Used? An Empirical Evaluation of Quantum Circuit Mutations