Ph.D. Candidate, Applied Mathematics

I am a fifth year Ph.D. candidate in Applied Mathematics in UC Berkeley, advised by Lin Lin. I did my undergrad in the Department of Mathematics, Peking University.

My research is centered on scientific machine learning, with various applications on quantum control, reinforcement learning and deep unsupervised learning.

Email: jiahao [at] math [dot] berkeley [dot] edu

Publications

2022

  1. [5]
    Jiahao Yao, Haoya Li, Marin Bukov, Lin Lin, Lexing Ying Monte Carlo Tree Search based Hybrid Optimization of Variational Quantum Circuits MSML 2022, In Proceedings of Machine Learning Research.

2021

  1. [4]
  2. [3]
    Jiahao Yao, Paul Köttering, Hans Gundlach, Lin Lin, Marin Bukov Noise-Robust End-to-End Quantum Control using Deep Autoregressive Policy Networks MSML 2021, In Proceedings of Machine Learning Research.

2020

  1. [2]
    Jiahao Yao, Marin Bukov, Lin Lin Policy Gradient based Quantum Approximate Optimization Algorithm MSML 2020, In Proceedings of Machine Learning Research.
  2. [1]
    Kevin J Sung, Jiahao Yao, Matthew P Harrigan, Nicholas C Rubin, Zhang Jiang, Lin Lin, Ryan Babbush, Jarrod R McClean Using models to improve optimizers for variational quantum algorithms Quantum Sci. Technol. 2020, In IOP Publishing.

Acknowledgements: based on the al-folio template modified by Tony Song.