Today, we face significant scientific challenges because the large-scale data acquired by our instrumentation and algorithm and the vast degrees of freedom of our target subjects are constantly defying human analysis. Here we sketch how machine learning techniques may serve as useful tools in overcoming data largeness and noisiness, in reverse thinking, and in bridging fields or even disciplines, such as between computation, experiment, and theory. We report developments in machine learning approaches in recognizing various phases from quantum many-body states and validating theory-hypothesized order hidden through large, complex electronic quantum matter data, and developing efficient and general quantum compiling algorithms at the dawn of quantum computation. We also outline our progress in using machine learning to analyze many-body Hamiltonian ground-state property from a novel perspective.

Dr. Zhang, Yi is a theoretical condensed matter physicist focusing on emergent phenomena and novel approaches in quantum materials, systems, and algorithms. He obtained his undergraduate degree at the Department of Physics, Fudan University, and his Ph.D. degree at UC Berkeley under advisor Prof. Ashvin Vishwanath. Afterward, Dr. Zhang moved to Stanford University as a SITP postdoctoral fellow and later joined Cornell University as a Bethe fellow. He joined International Center for Quantum Materials and School of Physics at Peking University in 2019 as a junior faculty member. Yi Zhang is interested in various quantum algorithm applications, including machine learning and quantum entanglement in quantum systems, theoretical characterizations and experimental properties of topological phases and materials, and various other topics. Dr. Zhang, Yi has published more than 40 papers in internationally recognizable journals, including Nature, Nature Physics, PRL, Nature communications, Nano Lett. and others. 
 

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