Yehui Tang (唐叶辉)

Yehui Tang (唐叶辉)

PhD candidate at SJTU

About me

Hello! I’m Yehui Tang (唐叶辉).

I’m a PhD candidate in Computer Science & Engineering (CSE) at Shanghai Jiao Tong University (SJTU), advised by Prof. Junchi Yan. Prior to this, I received my bachelor’s degree from Shandong University (SDU). My research topics include graph neural networks and generative models. Specifically, I am interested in developing robust learning systems of quantum many-body physics.

I am always open for collaboration opportunities and please feel free to contact me via email: yehuitang@sjtu.edu.cn

🔥 Recent News

  • [2025.01]: One paper is accepted to ICLR.
  • [2024.05]: One paper is accepted to ICML.
  • [2024.02]: One paper is accepted to CVPR. Congrats to Hao Xiong!
  • [2024.01]: Two papers are accepted by ICLR. One is accepted as a spotlight (top 5%).
  • [2022.09]: One paper is accepted to NeurIPS.

💻 Publications

📒 Topic: Efficiently Adapt Deep Learning Models to Solve Quantum Many-body Problems

  1. QuaDiM: A Conditional Diffusion Model For Quantum State Property Estimation
    Yehui Tang, Mabiao Long, Junchi Yan.
    ICLR (2025). [PDF]

  2. SSL4Q: Semi-Supervised Learning of Quantum Data with Application to Quantum State Classification
    Yehui Tang, Nianzu Yang, Mabiao Long, Junchi Yan.
    ICML (2024). [PDF]

  3. Towards LLM4QPE: Unsupervised Pretraining of Quantum Property Estimation and A Benchmark
    Yehui Tang, Hao Xiong, Nianzu Yang, Tailong Xiao, Junchi Yan.
    ICLR spotlight (2024). [PDF]

📒 Topic: Quantum Computing-Driven Artificial Intelligence

  1. Circuit Design and Efficient Simulation of Quantum Inner Product and Empirical Studies of Its Effect on Near-Term Hybrid Quantum-Classic Machine Learning
    Hao Xiong\(\dagger\), Yehui Tang\(\dagger\) (co-first author), Xinyu Ye\(\dagger\), Junchi Yan.
    CVPR (2024). [PDF] [Code]

  2. GraphQNTK: Quantum Neural Tangent Kernel for Graph Data
    Yehui Tang, Junchi Yan.
    NeurIPS (2022). [PDF] [Code]

  3. Towards a Native Quantum Paradigm for Graph Representation Learning: A Sampling-based Recurrent Embedding Approach
    Ge Yan, Yehui Tang, Junchi Yan.
    SIGKDD (2022). [PDF]

  4. Recent Progress and Perspectives on Quantum Computing for Finance
    Yehui Tang, Junchi Yan, Guoqiang Hu, Baohua Zhang, Jinzan Zhou.
    SOCA (2022). [PDF]

📒 Topic: Quantum-Inspired Machine Learning

  1. Node2ket: Efficient High-Dimensional Network Embedding in Quantum Hilbert Space
    Hao Xiong, Yehui Tang, Yunlin He, Wei Tan, Junchi Yan.
    ICLR (2024). [PDF] [Code]

Preprints

  1. Rethinking Video Tokenization: A Conditioned Diffusion-based Approach
    Nianzu Yang, Pandeng Li, Liming Zhao, Yang Li, Chen-Wei Xie, Yehui Tang, Xudong Lu, Zhihang Liu, Yun Zheng, Yu Liu, Junchi Yan.
    Arxiv (2025). [PDF]

  2. From Quantum Graph Computing to Quantum Graph Learning: A Survey
    Yehui Tang, Junchi Yan, Hancock Edwin.
    Arxiv (2022). [PDF]

🗞️ Academic Services

  • PC Member & Conference Reviewer: ICML 2025/2024/2023, ICLR 2025/2024/2023, NeurIPS 2024/2023/2022, CVPR 2024, AAAI 2025, ICCV 2025, SIGKDD 2023

📄 Patents

📖 Educations

  • 2020 - now, PhD, Shanghai Jiao Tong University, Shanghai, China
  • 2016 - 2020, Undergraduate, Shandong University, Jinan, China