A systematic analysis of 59 quantum software testing empirical studies reveals highly diverse designs, inconsistent reporting, and open methodological challenges, leading to recommendations for future work.
Qcrmut: Quantum circuit random mutant generator tool.arXiv preprint arXiv:2410.01415, 2024
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
years
2026 3roles
background 2polarities
background 2representative citing papers
QuanForge introduces statistical mutation killing and nine post-training mutation operators for QNNs to distinguish test suites and localize vulnerable circuit regions.
zkCraft combines LLM-guided mutations with R1CS-aware localization and Violation IOP proofs to detect under- and over-constrained faults in zero-knowledge circuits while reducing solver queries.
citing papers explorer
-
A Methodological Analysis of Empirical Studies in Quantum Software Testing
A systematic analysis of 59 quantum software testing empirical studies reveals highly diverse designs, inconsistent reporting, and open methodological challenges, leading to recommendations for future work.
-
QuanForge: A Mutation Testing Framework for Quantum Neural Networks
QuanForge introduces statistical mutation killing and nine post-training mutation operators for QNNs to distinguish test suites and localize vulnerable circuit regions.
-
zkCraft: Prompt-Guided LLM as a Zero-Shot Mutation Pattern Oracle for TCCT-Powered ZK Fuzzing
zkCraft combines LLM-guided mutations with R1CS-aware localization and Violation IOP proofs to detect under- and over-constrained faults in zero-knowledge circuits while reducing solver queries.