bat365中国在线平台官方网站学术报告（第74期）

报告题目：Learning quantum properties from short-range correlations using multi-task networks

报告专家：朱岩 博士    香港大学

报告时间：2024年10月31日（周四）14:00

报告地点：理6栋302      邀请人：张旦波

报告内容：

Characterizing multipartite quantum systems is crucial for quantum computing and many-body physics. The problem, however, becomes challenging when the system size is large and the properties of interest involve correlations among a large number of particles. Here we introduce a neural network model that can predict various quantum properties of many-body quantum states with constant correlation length, using only measurement data from a small number of neighboring sites. The model is based on the technique of multi-task learning, which we show to offer several advantages over traditional single-task approaches. Through numerical experiments, we show that multi-task learning can be applied to sufficiently regular states to predict global properties, like string order parameters, from the observation of short-range correlations, and to distinguish between quantum phases that cannot be distinguished by single-task networks. Remarkably, our model appears to be able to transfer information learnt from lower dimensional quantum systems to higher dimensional ones, and to make accurate predictions for Hamiltonians that were not seen in the training.

专家简介：

Dr. Yan Zhu is currently a Research Assistant Professor at the Quantum Information and Computation Initiative at the University of Hong Kong. He obtained a PhD in Computer Science from the University of Hong Kong and a Bachelor in Computer Science from Zhejiang University. His research interests include AI for Science and Quantum Machine Learning, with a recent focus on representation learning of quantum systems.