【直播】【复旦理论物理报告会】科罗拉多矿业大学龚哲轩教授：量子相变的无监督学习

直播信息

报告题目

量子相变的无监督学习

报告人（单位）

龚哲轩教授（科罗拉多矿业大学）

报告时间

2022年2月25日 10:00

主办方

复旦大学物理系

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报告摘要

Experimental quantum simulators have become large and complex enough that discovering new physics from the huge amount of measurement data can be quite challenging, especially when little theoretical understanding of the simulated model is available. Unsupervised machine learning methods are particularly promising in overcoming this challenge. I will review typical unsupervised learning methods and show that they generally only work for learning simple symmetry-breaking quantum phase transitions. I will then show that a more advanced method known as diffusion map, which performs nonlinear dimensionality reduction and spectral clustering of the measurement data, has much better potential for unsupervised learning of complex phase transitions, such as topological phase transitions and many-body localization. This method is readily applicable to many experimental quantum simulators as it only requires measuring each particle in a single and local basis.

报告人介绍

Zhexuan Gong (龚哲轩) obtained his PhD in physics from University of Michigan in 2013. He then spent four years working as a research associate and a research scientist at the Joint Quantum Institute associated with the University of Maryland and the NIST. He joined Mines as an assistant professor of physics in 2018 and also holds an associate position at NIST. Gong's research is in the field of theoretical quantum physics. In particular, he is interested in designing faster architectures for quantum computing, understanding novel quantum many-body physics via quantum simulation, and applying machine learning to facilitate new discoveries in quantum experiments. He recently won a prestigious W. M. Keck Foundation award to support his research in designing a next-generation quantum simulation platform.

复旦大学物理系理论物理报告会

复旦理论物理报告会，每周五上午9:30举行，报告时间为一到两个小时。报告会旨在加强国内外理论物理前沿研究进展的互动，着重凝聚态物理、量子信息和原子分子光学等学科中重要的理论物理方面的最新进展和发展方向。通过加强交流，促进不同物理学科的交叉互通， 启发新的研究角度和新科学思想。提高研究生对前沿理论物理研究动向的敏感度和思考新物理问题尤其是交叉学科问题的积极性。

专题直播&回放链接：

https://www.koushare.com/topicIndex/i/phys-fudan

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编辑：王茹茹

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