# Quantum Software Testing

Quantum programs are judged by probabilistic outputs and domain-specific checkers, so a syntactically valid test can still disagree with the benchmark verdict. QTestBench has models write syntactic, semantic, and behavioral pytest modules per task, injects candidate solutions, and compares each test verdict against the benchmark checker rather than the tests themselves across three validation cohorts. On eight PennyLane QHack challenges and ten Qiskit HumanEval tasks, generated suites almost always execute, yet discrimination lags: the best test quality is 0.71 on QHack and 0.34 on Qiskit HumanEval, so executability alone overstates test quality.

Quantum programs are judged by probabilistic outputs and domain-specific checkers, so a syntactically valid test can still disagree with the benchmark verdict. QTestBench has models write syntactic, semantic, and behavioral pytest modules per task, injects candidate solutions, and compares each test verdict against the benchmark checker rather than the tests themselves across three validation cohorts. On eight PennyLane QHack challenges and ten Qiskit HumanEval tasks, generated suites almost always execute, yet discrimination lags: the best test quality is 0.71 on QHack and 0.34 on Qiskit HumanEval, so executability alone overstates test quality.

The authors present QuSema, an autonomous testing agent that uses quantum semantics and documentation as a source-level semantic oracle, running a three-stage loop of contextual unit preparation, semantic defect detection, and library API triggering and validation to decide whether implementation logic can turn valid inputs into invalid outputs; on a benchmark of 20 historical silent bugs in Qiskit and PennyLane it achieves higher mean bug relocation counts than Claude Code and Codex, and it discovers 40 previously unknown bugs confirmed by maintainers, 30 of them silent.

The authors present QuSema, an autonomous testing agent that uses quantum semantics and documentation as a source-level semantic oracle, running a three-stage loop of contextual unit preparation, semantic defect detection, and library API triggering and validation to decide whether implementation logic can turn valid inputs into invalid outputs; on a benchmark of 20 historical silent bugs in Qiskit and PennyLane it achieves higher mean bug relocation counts than Claude Code and Codex, and it discovers 40 previously unknown bugs confirmed by maintainers, 30 of them silent.