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2024.md

@@ -54,7 +54,7 @@ tags = ["2024", "juliacn", "meetup", "winter", "见面会", "冬季", "program",
 <td title="Tensor network machine learning models are versatile in addressing complex data-driven tasks， ranging from quantum many-body problems to classical pattern recognitions. Despite their promising performance， understanding the underlying assumptions and limitations of these models remains largely unexplored. In this work， we focus on the exact formulation of no-free-lunch theories for tensor network-based machine learning models. Specifically， we rigorously analyze the generalization risks of learning target output functions from input data encoded in tensor network states. We first prove the no-free-lunch theory for the machine learning model based on the matrix product states， i.e.， the 1D tensor network states. Furthermore， to show the validity of our theoretical framework in various tensor network models， we prove the no-free-lunch theory for the case of 2D projected entangled-pair state， by introducing the combinatorial method associated to the “puzzle of polyominoes”. Our findings shed light on the intrinsic limitations of tensor network-based learning models in a rigorous fashion， and open up an avenue for further analytical research on exploring the advantages of quantum-inspired machine learning models.">在张量网络机器学习模型中制定无免费午餐理论, <strong>于立伟</strong>，特聘研究员，南开大学陈省身数学研究所</td>
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-<td>15:00AM-15:40AM </td>
+<td>3:00PM-3:40PM </td>
 <td title="量子速度极限是量子力学中的一个基本概念，它描述了量子态实现指定演化目标的速度上限， 在量子计算、量子计量学和量子控制等领域具有广泛的应用潜力。 报告将尝试介绍量子速度极限的基本原理和数值计算。">Julia 与量子速度极限101, <strong>余怀明</strong>，在读学生，华中科技大学物理学院</td>
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