Testing quantum programs in a black-box setting is difficult because bug-relevant behavioral differences may be missed at two levels: the chosen inputs may fail to trigger faulty behavior, and even when faults are triggered, the chosen measurements may fail to make the resulting differences observable. Existing methods have mainly strengthened either input generation or measurement design in isolation, leaving their coupling insufficiently addressed. In this paper, we present QDoctor, a probe-based black-box testing framework that wraps the program under test with a variety of property-based probes, thereby effectively extracting program information and exploring its behavior. A probe integrates two components: test-case generation and the specification of the measurement basis. To reduce false positive rate, QDoctor also supports semantic-aware partial equivalence testing through constrained probe-oracle design. Experimental results on benchmark suites show that QDoctor achieves the best balanced performance among representative black-box baselines, reaching 99.89% recall, 98.23% precision, 99.06% F1, and 0.9988 AUC. Ablation shows that jointly strengthening inputs and measurements raises recall from 85.280% to 99.728% with zero false positives, while semantic probes drive the false-positive rate to zero on SWAP-induced equivalent mutants.

@inproceedings{paperbot12586,
  title = {QDoctor: Probe-Based Testing for Quantum Programs},
  author = {Qichen Wang and Shangzhou Xia and Xiaoyu Guo and Jianjun Zhao},
  booktitle = {ASE},
  year = {2026},
  doi = {10.1145/3832783.3837488}
}