Academic Activities

Dongling Deng The results are published in Nature Physics

The research group of Dongling Deng at the Institute for Interdisciplinary Information Sciences, Tsinghua University and Shanghai Qi Zhi Institue, in collaboration with the research groups led by Prof. Haohua Wang and Prof. Chao Song from the School of Physics at Zhejiang University, have created the long-sought-after non-Abelian Fibonacci topologically-ordered states and successfully simulated braidings of Fibonacci anyons for the first time with a programmable superconducting processor. Their paper, “Non-Abelian braiding of Fibonacci anyons with a superconducting processor”, was recently published in "Nature Physics."

Xiaopeng Li The results are published in Phys. Rev. Letter

Xiaopeng Li's team has conducted research on using artificial intelligence to enhance the detection sensitivity of quantum precision measurement. They proposed a brand-new, fully data-driven method to enhance the detection sensitivity to weak signals, without relying on any prior knowledge or assumptions about the physical system or sensing process.

Xiaopeng Li The results are published in Communication Physics

To perform general tasks in quantum neural networks, the introduction of nonlinear functions is necessary. Current quantum neural networks are not very good at handling this complexity, which is also a problem that quantum machine learning urgently needs to address. In response to this issue, Li Xiaopeng proposed introducing randomness into quantum neural networks and proposed a new type of quantum neural network structure (Quantum Neural Networks, QNNs), expanding the expressive power of quantum neural networks.

Yu Yu The results are published in USENIX Security 2024

Yu Yu has recently achieved a series of important research results in the field of cybersecurity and privacy protection. He proposed a lightweight web data certification technology that significantly improves the communication and computational efficiency of the TLS protocol. He also designed a scalable multi-party computation protocol for privacy-preserving machine learning, optimizing efficiency and scalability under the honest majority setting. These achievements are of significant value to the directions of secure multi-party computation and privacy-preserving machine learning. Two related results have been included in USENIX Security 2024.