Publications
2026
- G Yan, S Li, Y Du.
“Rethink the Role of Neural Decoders in Quantum Error Correction.”
In International Conference on Machine Learning (ICML), 2026.
[arXiv]
- H Cao, G Yan, Y Du, F Pan.
“Maximum Likelihood Decoding of Quantum Error Correction Codes.”
arXiv preprint arXiv:2605.17230, 2026.
[arXiv]
- G Yan, Y Wang, J Yan.
“Towards Real-Time Neutral Atom Array Assembly via Unsupervised Hologram Generation and Path Optimization.”
In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 40 (32), 27486, 2026.
2025
- R Wang, Z Xia, G Yan, J Yan.
“Quanonet: Quantum neural operator with application to differential equation.”
In International Conference on Machine Learning (ICML), 2025.
- WY Liao, G Yan, Y Song, TC Tian, WM Zhu, DT Jiang, Y Du, HL Huang.
“Sample-efficient quantum error mitigation via classical learning surrogates.”
arXiv preprint arXiv:2511.07092, 2025.
[arXiv]
2024
- G Yan, W Wu, Y Chen, K Pan, X Lu, Z Zhou, Y Wang, R Wang, J Yan.
“Quantum circuit synthesis and compilation optimization: Overview and prospects.”
arXiv preprint arXiv:2407.00736, 2024.
[arXiv]
- W Wu, Y Wang, G Yan, Y Zhao, B Zhang, J Yan.
“On reducing the execution latency of superconducting quantum processors via quantum job scheduling.”
In Proceedings of the 43rd IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2024.
- G Yan, H Chen, K Pan, J Yan.
“Rethinking the symmetry-preserving circuits for constrained variational quantum algorithms.”
In The Twelfth International Conference on Learning Representations (ICLR), 2024.
- G Yan, K Pan, R Wang, M Ran, H Chen, X Wang, J Yan.
“Universal Hamming Weight Preserving Variational Quantum Ansatz.”
arXiv preprint arXiv:2412.04825, 2024.
[arXiv]
- G Yan, M Ran, R Wang, K Pan, J Yan.
“Rethinking parity check enhanced symmetry-preserving ansatz.”
In Advances in Neural Information Processing Systems (NeurIPS), 37, 65020-65047, 2024.
2023
- W Wu, G Yan, X Lu, K Pan, J Yan.
“Quantumdarts: differentiable quantum architecture search for variational quantum algorithms.”
In International Conference on Machine Learning (ICML), 37745-37764, 2023.
- X Lu, K Pan, G Yan, J Shan, W Wu, J Yan.
“Qas-bench: rethinking quantum architecture search and a benchmark.”
In International Conference on Machine Learning (ICML), 22880-22898, 2023.
- X Ye, G Yan, J Yan.
“Towards quantum machine learning for constrained combinatorial optimization: a quantum qap solver.”
In International Conference on Machine Learning (ICML), 39903-39912, 2023.
- G Yan, H Wu, J Yan.
“Quantum 3d graph learning with applications to molecule embedding.”
In International Conference on Machine Learning (ICML), 39126-39137, 2023.
- X Ye, G Yan, J Yan.
“Vqne: Variational quantum network embedding with application to network alignment.”
In Proceedings of the 29th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2023.
2022 & Before
- G Yan, Y Tang, J Yan.
“Towards a native quantum paradigm for graph representation learning: A sampling-based recurrent embedding approach.”
In Proceedings of the 28th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2022.
