| [1] | Ji Guan and Mingsheng Ying. Verifying adversarial robustness in quantum machine learning: From theory to physical validation via a software tool. In Quantum Robustness in Artificial Intelligence. Springer, 2026. [ DOI | http | arXiv ] |
| [2] | Ji Guan. Optimal mechanisms for quantum local differential privacy. In Proceedings of the 2025 ACM SIGSAC Conference on Computer and Communications Security. ACM, 2025. [ DOI | http ] |
| [3] | Hai-Feng Zhang, Zhao-Yun Chen, Peng Wang, Liang-Liang Guo, Tian-Le Wang, Xiao-Yan Yang, Ren-Ze Zhao, Ze-An Zhao, Sheng Zhang, Lei Du, Hao-Ran Tao, Zhi-Long Jia, Wei-Cheng Kong, Huan-Yu Liu, Athanasios V. Vasilakos, Yang Yang, Yu-Chun Wu, Ji Guan, Peng Duan, and Guo-Ping Guo. Experimental robustness benchmarking of quantum neural networks on a superconducting quantum processor. SCIENCE CHINA Physics, Mechanics & Astronomy, 2025. [ DOI | arXiv ] |
| [4] | Yanling Lin, Ji Guan, Wang Fang, Mingsheng Ying, and Zhaofeng Su. VeriQR: A robustness verification tool for quantum machine learning models. In Formal Methods - 26th International Symposium, FM 2024, Lecture Notes in Computer Science. Springer, 2024. [ DOI | http ] |
| [5] | Ji Guan, Wang Fang, Mingyu Huang, and Mingsheng Ying. Detecting violations of differential privacy for quantum algorithms. In Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security. ACM, 2023. [ DOI | http ] |
| [6] | Ji Guan, Wang Fang, and Mingsheng Ying. Verifying fairness in quantum machine learning. In Computer Aided Verification - 34th International Conference, CAV, volume 13372 of Lecture Notes in Computer Science, pages 408–429. Springer, 2022. [ DOI | http ] |
| [7] | Ji Guan, Wang Fang, and Mingsheng Ying. Robustness verification of quantum classifiers. In Computer Aided Verification - 33rd International Conference, CAV 2021, volume 12759 of Lecture Notes in Computer Science, pages 151–174. Springer, 2021. [ DOI | http ] |
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