研究方向

量子信息、量子机器学习、量子错误缓释

教育背景

2019.9-2024.6 博士 清华大学交叉信息研究院

2015.8-2019.6 学士 中山大学数据科学与计算机学院

联系邮箱

linxiaodie0923@gmail.com

发表论文

[1] Chen, Z., Lin, L.,Lin, X.^(𝛼-𝛽), Wei, Z., & Yao, P. (2024). The generations of classical correlations via quantum schemes. IEEE Transactions on Information Theory, 70(6), 4160- 4169.

[2]Lin, X., Chen, Z., & Wei, Z. (2023). Quantifying quantum entanglement via a hybrid quantum-classical machine learning framework. Physical Review A, 107(6), 062409.

[3]Lin, X., Chen, Z., & Wei, Z. (2023). Quantifying unknown entanglement by neural networks. Quantum Information Processing, 22(9), 341.

[4] Chen, Z.,Lin, X., & Wei, Z. (2023). Certifying unknown genuine multipartite entangle- ment by neural networks. Quantum Science and Technology, 8(3), 035029.

[5] Guo, Y., Lin, L., Cao, H., Zhang, C.,Lin, X., Hu, X. M., ...& Guo, G. C. (2023). Experimental entanglement quantification for unknown quantum states in a semi-device- independent manner. Science China Information Sciences, 66(8), 180506.

[6] Lin, X.^(𝛼-𝛽), Wei, Z., & Yao, P. (2021). Quantum and classical hybrid generations for classical correlations. IEEE Transactions on Information Theory, 68(1), 302-310.

[7] Zhang, L.*,Lin, X.*, Wang, P., Yang, K., Zeng, X., Wei, Z., & Wang, Z. (2024). Variational optimization for quantum problems using deep generative networks. arXiv:2404.18041.

[8] Lin, L., Chen, Z.,Lin, X., & Wei, Z. (2023). All pure bipartite entangled states can be semi-self-tested with only one measurement setting on each party. arXiv:2306.07755.

[9] 林小蝶, 魏朝晖. (2023). 经典、量子及其混合场景下的经典关联生成协议 [J]. 电子科 技大学学报, 2023, 52(1): 2-7.