| Using models to improve optimizers for variational quantum algorithms KJ Sung, J Yao, MP Harrigan, NC Rubin, Z Jiang, L Lin, R Babbush, ... Quantum Science and Technology 5 (4), 044008, 2020 | 85 | 2020 |
| Reinforcement learning for many-body ground-state preparation inspired by counterdiabatic driving J Yao, L Lin, M Bukov Physical Review X 11 (3), 031070, 2021 | 71 | 2021 |
| Policy gradient based quantum approximate optimization algorithm J Yao, M Bukov, L Lin Mathematical and scientific machine learning, 605-634, 2020 | 56 | 2020 |
| Monte carlo tree search based hybrid optimization of variational quantum circuits J Yao, H Li, M Bukov, L Lin, L Ying Mathematical and Scientific Machine Learning, 49-64, 2022 | 11 | 2022 |
| Noise-robust end-to-end quantum control using deep autoregressive policy networks J Yao, P Kottering, H Gundlach, L Lin, M Bukov Mathematical and scientific machine learning, 1044-1081, 2022 | 9 | 2022 |
| Impact: Importance weighted asynchronous architectures with clipped target networks M Luo, J Yao, R Liaw, E Liang, I Stoica arXiv preprint arXiv:1912.00167, 2019 | 8 | 2019 |
| Inventing painting styles through natural inspiration N Abrahamsen, J Yao arXiv preprint arXiv:2305.12015, 2023 | 2 | 2023 |
| Rl-qaoa: a reinforcement learning approach to many-body ground state preparation J Yao, L Lin, M Bukov APS March Meeting Abstracts 2021, V32. 012, 2021 | 1 | 2021 |
| Random coordinate descent: a simple alternative for optimizing parameterized quantum circuits Z Ding, T Ko, J Yao, L Lin, X Li arXiv preprint arXiv:2311.00088, 2023 | | 2023 |
| Reinforcement Learning and Variational Quantum Algorithms J Yao UC Berkeley, 2023 | | 2023 |
Lin LinUniversity of California, BerkeleyVerified email at math.berkeley.edu
