电子邮件: guanj@ios.ac.cn
通信地址: 北京市海淀区中关村南四街4号中国科学院软件研究所
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研究领域
量子计算;量子机器学习;模型检测
招生信息
招生专业
招生方向
教育背景
工作经历
工作简历
社会兼职
荣誉
2023年中国科学院青年创新促进会会员
2023年北京市高层次留学人才回国资助
专利成果
出版信息
代表论文
主要研究
1.研究和发展可信量子人工智能的形式化检测的理论和方法:定义了一个用于量子人工智能学习模型的公平性和鲁棒性验证和分析的形式化框架,并开发了算法来形式化检测这两个性质。这些相关成果作为量子计算领域仅有的两篇被接受的工作已经发表在国际计算机辅助验证最好的(CCF 理论计算机A类)会议CAV 2021 和 2022 上,这项工作开创了一个新的研究方向,相信未来也将激发越来越多的研究。
2. 联合领导一个名为 VeriQ 的可信量子计算工具链的开发项目,其中包括 (i) QDA(量子计算设计自动化)、(ii) 量子程序验证和分析以及 (iii) 可信量子人工智能。 特别是参与实现了 (a) VeriQRobust 用于量子分类器的稳健性验证,(b) VeriQFair用于量子决策模型的公平性验证,以及 (c) VeriQBench 用于多种类型的量子电路的基准测试。VeriQ也是国际上第一个较为完整的可信量子计算的工具链。
3. 量子马尔科夫链的状态空间周期分解及其应用:量子马尔可夫链理论上是经典马尔可夫链的推广,可以用来模拟量子系统的行为。其状态空间分解技术是分析量子系统行为的关键工具。一个量子马尔可夫链的状态空间,首先可以分解成若干个相互正交的量子底层强连通子空间和一个正交的瞬态子空间;接着,每个底层强连通子空间可以根据其周期性质分解成相互正交的子空间,并且这些子空间在量子系统演化中会周期性交换。此类分解可以用于量子通讯系统中,去找到最大维度的不受量子噪声影响的信道,用于信息的可靠传输。以上工作总结在论文《Decomposition of Quantum Markov Chains and Its Applications》中,于2018年发表在国际知名期刊《Journal of Computer and Systems Sciences》,并之后在2022年被整理在国际上第一本量子模型检测专著《Model Checking Quantum Systems: Principles and Algorithms》中。同时,这一量子马尔科夫链的周期分解可以被用来证明量子纠缠可以超级激活量子存储,也就是两个无法存储量子信息的存储器可以并联存储量子信息。此工作总结在论文《Supper-activating Quantum Memory with Entanglement》中,于2018年发表在国际知名期刊《Quantum Information and Computation》上。更进一步,我们将状态空间周期分解应用于量子染色问题(经典染色问题的量子推广)中,提出出对于2染色问题的算法,并实际应用在量子信息的保护中。此工作总结在论文《From independent sets and vertex colorings to isotropic spaces and isotropic decompositions: Another bridge between graphs and alternating matrix spaces》于2021年被国际顶级期刊 《SIAM Journal on Computing》接收。其会议版本接收在2020年11th Innovations in Theoretical Computer Science Conference (ITCS 2020) 。
科研项目
发表论文
会议论文
1. Ji Guan*, Wang Fang, Mingyu Huang, and Mingsheng Ying. 2023. Detecting Violations of Differential Privacy for Quantum Algorithms. In Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security (CCS ’23), November 26–30, 2023, Copenhagen, Denmark. ACM, New York, NY, USA, 17 pages. https://doi.org/10.1145/3576915.3623108
2. Guan J.*, Fang, W. and Ying, M., 2022. Verifying Fairness in Quantum Machine Learning. In International Conference on Computer Aided Verification (pp. 408-429). Springer, Cham. (The only quantum paper in CAV 2022)
3. Guan, J., Yu, N. (2022). A Probabilistic Logic for Verifying Continuous-time Markov Chains. In: Fisman, D., Rosu, G. (eds) Tools and Algorithms for the Construction and Analysis of Systems. TACAS 2022. Lecture Notes in Computer Science, vol 13244. Springer, Cham.
4. Guan J.*, Fang W., Ying M. (2021) Robustness Verification of Quantum Classifiers. In: Silva A., Leino K.R.M. (eds) Computer Aided Verification. CAV 2021. Lecture Notes in Computer Science, vol 12759. Springer, Cham. (The only quantum paper in CAV 2021)
5. Xu M., Mei J., Guan J.*, and Yu N. “Model Checking Quantum ContinuousTime Markov Chains,” in 32nd International Conferenceon Concurrency Theory (CONCUR 2021), ser. Leibniz International Proceedings in Informatics (LIPIcs), S. Haddad and D. Varacca, Eds., vol. 203. Dagstuhl, Germany: Schloss Dagstuhl – Leibniz-Zentrum f¨ur Informatik, 2021, pp. 13:1-13:17. (The only quantum paper in CONCUR 2021)
6. Bei, X., Chen, S., Guan, J., Qiao, Y. and Sun, X., 2020. From Independent Sets and Vertex Colorings to Isotropic Spaces and Isotropic Decompositions: Another Bridge Between Graphs and Alternating Matrix Spaces. In 11th Innovations in Theoretical Computer Science Conference (ITCS 2020). Schloss Dagstuhl-LeibnizZentrum f¨ur Informatik.
期刊论文
1. Wang, Q., Zhang, Z., Chen, K., Guan, J.*, Fang, W., Liu, J. and Ying, M., 2022. Quantum algorithm for fidelity estimation. IEEE Transactions on Information Theory, 69(1), pp.273-282.
2. Bei, X., Chen, S., Guan, J., Qiao, Y. and Sun, X., 2021. From independent sets and vertex colorings to isotropic spaces and isotropic decompositions: Another bridge between graphs and alternating matrix spaces. SIAM Journal on Computing, 50(3), pp.924-971.
3. Guan, J.*, Wang, Q. and Ying, M., 2021. An HHL-Based Algorithm for Computing Hitting Probabilities of Quantum Random Walks. Quantum Information & Computation 21(5&6): 0395-0408 (2021).
4. Guan, J.*, Feng, Y. and Ying, M., 2018. Decomposition of quantum Markov chains and its applications. Journal of Computer and System Sciences, 95, pp.55-68.
5. Guan, J.*, Feng, Y. and Ying, M., 2018. Super-activating Quantum Memory with Entanglement. Quantum Information & Computation 18(13&14): 1115-1124 (2018).
6. Su, Z., Guan, J. and Li, L., 2018. Efficient quantum repeater with respect to both entanglement-concentration rate and complexity of local operations and classical communication. Physical Review A, 97(1), p.012325.
7. Liu, S., Wang, X., Zhou, L., Guan, J., Li, Y., He, Y., Duan, R. and Ying, M., 2018. Q | SI ⟩ : A Quantum Programming Environment. In Symposium on Real-Time and Hybrid Systems (pp. 133-164). Springer, Cham.
8. Liu S S, Zhou L, Guan J, et al. Q | SI ⟩ : a Quantum Programming Environment (in Chinese). Sci Sin Inform, 2017, 47: 1300–1315, doi: 10.1360/N112017-00095