Machine Learning Methods in Quantum Physics

演讲人: Pei-Lin Zheng Peking University
时间: 2021-10-13 19:00-2021-10-13 20:00
地点:MMW S527 +Online (Tencent Meeting App: 849 705 152 Password: 1984)

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.


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.


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