Search-Based Generation of Undetected Quantum Circuit Mutants
2026-08-10 • Software Engineering
Software Engineering
AI summaryⓘ
The authors looked at how to test quantum programs better by creating tricky mistakes (mutants) for testing. They found that existing methods make mutants that are too easy to catch, so they created QUMUG, a tool that uses adjustable parts of quantum gates to make harder-to-detect mutants. By using search algorithms, especially a genetic algorithm, their method produced mutants that were much tougher for test suites to find, meaning test suites need more improvements. They also explored how combining multiple mutations affects testing and found that first-level mutations help most with test coverage, while more complex ones show the need for new test cases.
Quantum mutation analysisQuantum gatesTest suitesGenetic algorithmMutantsParameterisable gatesNon-equivalent mutantsHigher order mutantsQuantum circuits
Authors
Eñaut Mendiluze Usandizaga, Thomas Laurent, Paolo Arcaini, Shaukat Ali
Abstract
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.