Abstract:

In quantum physics, we usually face exponentially large degrees of freedom and the large-scale data obtained from quantum systems, which constantly defy our analysis capability. Here, we sketch how machine learning may become a valuable tool in overcoming huge amounts of data and degrees of freedom and reverse thinking, which builds a meaningful bridge between computation power and physical intuition. We outline our recent developments on efficient and general algorithms based upon machine learning for quantum compiling and ground-state properties of quantum many-body Hamiltonians, which provide a new perspective for intriguing applications of machine learning in quantum physics.

Short Bio:

Pei-Lin Zheng is a PhD student in the International Center for Quantum Materials, School of Physics, Peking University. His research interests include machine learning, quantum algorithm design and quantum many-body physics.

Reference:

[1] P.-L. Zheng, S.-J. Du, and Y. Zhang. "Ground-state properties via machine learning quantum constraints," arXiv:2105.09947 (2021).

[2] Y.-H. Zhang, P.-L. Zheng, Y. Zhang, and D.-L. Deng, “Topological Quantum Compiling with Reinforcement Learning,” Phys. Rev. Lett. 125, 170501 (2020).

[3] Y. Zhang and E.-A. Kim, “Quantum Loop Topography for Machine Learning,” Phys. Rev. Lett. 118, 216401 (2017).