To take advantage of quantum devices/hardware for useful application, it remains a question whether a quantum algorithm can be “efficiently” emulated within non-ideal quantum devices in the presence of environmental noises. Such limitations may be mitigated by studying the following topics: control algorithms for quantum systems; algorithms for quantum circuit synthesis; algorithms for quantum circuit simulation; hybrid quantum-classical algorithms for near term applications; neural network and other machine learning techniques for optimization tasks in quantum information processing; quantum error correction techniques for long-term quantum architecture. Collaboration with experimentalists and engineers (on both hardware and software) is widely explored.
Prof. Zeng received her Bachelor of Science in Physics and Mathematics, and Master of Science in Physics from Tsinghua University in 2002 and 2004 respectively. Then she pursued her PhD in Physics at Massachusetts Institute of Technology (MIT) and graduated in 2009. From 2009 to 2010, she worked as a Postdoctoral Fellow at the Institute for Quantum Computing (IQC) and the Department of Combinatorics & Optimization at the University of Waterloo. In 2010, Prof. Zeng joined the Department of Mathematics & Statistics at the University of Guelph as an Assistant Professor, and promoted to Tenured Associate Professor in 2014 and Professor in 2018. Since 2019, she has become a Professor in the Department of Physics at The Hong Kong University of Science and Technology.
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