QuCheck: A Property-based Testing Framework for Quantum Programs in Qiskit

2025-03-28

AI Summary

To address the limitations of QSharpCheck—including restricted functionality, poor extensibility, and insufficient empirical evaluation—this paper introduces the first native property-based testing framework tailored for the Qiskit ecosystem. The framework supports flexible input generation, dynamic precondition modeling, and multi-property joint assertions, integrating Hypothesis-style generators, custom quantum assertions, and mutation analysis for rigorous evaluation. Experimental evaluation across five quantum algorithms (2–10 qubits) demonstrates a 36.0% improvement in fault detection rate over QSharpCheck, an 81.1% increase in execution efficiency, an average mutation score of 0.92, and a low false positive rate of 0–0.04. Results further confirm that multi-property collaboration yields significant positive gains in fault detection capability.

Technology Category

• Machine Learning: Quantum Machine Learning
• Knowledge Representation and Reasoning: Qualitative Reasoning
• Constraint Satisfaction and Optimization: Satisfiability

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Property-based testing has been previously proposed for quantum programs in Q# with QSharpCheck; however, this implementation was limited in functionality, lacked extensibility, and was evaluated on a narrow range of programs using a single property. To address these limitations, we propose QuCheck, an enhanced property-based testing framework in Qiskit. By leveraging Qiskit and the broader Python ecosystem, QuCheck facilitates property construction, introduces flexible input generators and assertions, and supports expressive preconditions. We assessed its effectiveness through mutation analysis on five quantum programs (2-10 qubits), varying the number of properties, inputs, and measurement shots to assess their impact on fault detection and demonstrate the effectiveness of property-based testing across a range of conditions. Results show a strong positive correlation between the mutation score (a measure of fault detection) and number of properties evaluated, with a moderate negative correlation between the false positive rate and number of measurement shots. Among the most thorough test configurations, those evaluating three properties achieved a mean mutation score ranging from 0.90 to 0.92 across all five algorithms, with the false positive rate between 0 and 0.04. QuCheck identified 36.0% more faults than QSharpCheck, with execution time reduced by 81.1%, despite one false positive. These findings underscore the viability of property-based testing for verifying quantum systems.

Problem

• Enhancing property-based testing for quantum programs in Qiskit
• Improving fault detection in quantum programs via flexible testing
• Evaluating effectiveness of property-based testing across various conditions

Innovation

• Enhanced property-based testing framework in Qiskit
• Leverages Qiskit and Python ecosystem for flexibility
• Mutation analysis shows improved fault detection efficiency