update

2024.md

@@ -20,6 +20,7 @@ tags = ["2024", "juliacn", "meetup", "winter", "见面会", "冬季", "program",
 ##### 11月1日 星期五 (注册日)
 ~~~
 <table style="width:90%">
+<tr><td></td><td>Chair: Jin-Guo Liu</td></tr>
 <tr>
 <td width=23%>7:00PM-9:00PM</td>
 <td title="本次教程将从基本的高性能计算要点出发，介绍一些业界典型的落地实践方案，同时也将介绍一些与 AI 有关的性能优化工作。推荐有一定编程经验的同学参加。">新手教程: 高性能计算导引及其在 AI 中的应用, <strong>陈久宁</strong>, 苏州同元软控技术有限公司</td>
@@ -38,6 +39,7 @@ tags = ["2024", "juliacn", "meetup", "winter", "见面会", "冬季", "program",
 ##### 11月2日 星期六 (W-4, 1楼, 101)
 ~~~
 <table style="width:90%">
+<tr><td></td><td>Chair: Xuan-Zhao Gao</td></tr>
 <tr>
 <td width=23%>10:00AM-10:40AM </td>
 <td title="Quantum nonlocality describes a stronger form of quantum correlation than that of entanglement. It refutes Einstein's belief of local realism and is among the most distinctive and enigmatic features of quantum mechanics. It is a crucial resource for achieving quantum advantages in a variety of practical applications， ranging from cryptography and certified random number generation via self-testing to machine learning. Nevertheless， the detection of nonlocality， especially in quantum many-body systems， is notoriously challenging. Here， we report an experimental certification of genuine multipartite Bell correlations， which signal nonlocality in quantum many-body systems， up to 24 qubits with a fully programmable superconducting quantum processor. In particular， we employ energy as a Bell correlation witness and variationally decrease the energy of a many-body system across a hierarchy of thresholds， below which an increasing Bell correlation depth can be certified from experimental data. As an illustrating example， we variationally prepare the low-energy state of a two-dimensional honeycomb model with 73 qubits and certify its Bell correlations by measuring an energy that surpasses the corresponding classical bound with up to 48 standard deviations. In addition， we variationally prepare a sequence of low-energy states and certify their genuine multipartite Bell correlations up to 24 qubits via energies measured efficiently by parity oscillation and multiple quantum coherence techniques. In parallel， we present an optimization scheme to improve nonlocality certification by exploring flexible mappings between Bell inequalities and Hamiltonians corresponding to the Bell operators. We show that several Hamiltonian models can be mapped to new inequalities with improved classical bounds than the original one， enabling a more robust detection of nonlocality. From the other direction， we investigate the mapping from fixed Bell inequalities to Hamiltonians， aiming to maximize quantum violations while considering experimental imperfections. Our results establish a viable approach for preparing and certifying multipartite Bell correlations， which provide not only a finer benchmark beyond entanglement for quantum devices， but also a valuable guide towards exploiting multipartite Bell correlation in a wide spectrum of practical applications.">通过变分量子算法探测多体贝尔关联, <strong>李炜康</strong>，在读博士生，清华大学交叉信息研究院</td>
@@ -46,8 +48,9 @@ tags = ["2024", "juliacn", "meetup", "winter", "见面会", "冬季", "program",
 <td>11:00AM-11:40AM </td>
 <td title="由于物理场在空间和时间上可能表现出显著的不均匀性，静态网格可能导致计算效率低下或较大的数值误差。自适应网格(AMR)根据解的演化特征重新分配计算资源，能够实现效率与精度之间的平衡。同时，基于树的笛卡尔网格能够灵活描述计算域的边界和间断解，在自动化、普适性方面具备优势。 KitAMR.jl专注于基于动理学方程的数值格式，期望给出近平衡流域流动的准确数值描述。由于非平衡流动通常伴随解在物理和速度空间的集中分布和剧烈变化，网格的自适应是十分必要的。基于P4est.jl和MPI.jl，我们开发了一个基于四叉树/八叉树的分布式求解器，用于求解跨流域的多尺度复杂流动。报告将简单介绍理论基础，通过全面的基准测试展示求解器的实用性，并在最后讨论使用Julia开发的优缺点和未来的展望。">KitAMR.jl: 分布式、自适应的非平衡流动求解器, <strong>葛龙庆</strong>，在读博士生，北京大学工学院力学与工程科学系，湍流与复杂系统国家重点实验室，应用物理与技术研究中心</td>
 </tr>
+<tr><td></td><td>Chair: Guo-Yi ZHU</td></tr>
 <tr>
-<td>2:00PM-2:40PM </td>
+<td>2:00PM-2:40PM</td>
 <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>
 </tr>
 <tr>
@@ -64,6 +67,7 @@ tags = ["2024", "juliacn", "meetup", "winter", "见面会", "冬季", "program",
 ##### 11月3日 星期日 (W-4, 1楼, 101)
 ~~~
 <table style="width:90%">
+<tr><td></td><td>Chair: Yu-Sheng ZHAO</td></tr>
 <tr>
 <td width=23%>10:00AM-10:40AM </td>
 <td title="边值微分代数方程是边值问题在有代数约束下的一类数学模型，如何高效的求解此类方程一直是科学计算中的重要问题，然而目前流行的开源或商业微分方程求解器对此类问题直接求解的支持极少，这也是目前科学计算软件中的一个痛点，本报告将介绍SciML生态中的边值微分代数方程求解器的开发与相关边值问题求解器的最新进展。">边值微分代数方程的配点法求解器, <strong>曲庆宇</strong>，硕士研究生，浙江大学</td>
@@ -74,7 +78,7 @@ tags = ["2024", "juliacn", "meetup", "winter", "见面会", "冬季", "program",
 </tr>
 <tr>
 <td>2:00PM-6:00PM </td>
-<td>Hackathon</td>
+<td>Hackathon (<a href="https://github.com/JuliaCN/meetup-website/issues/8">Issue list</a>)</td>
 </tr>
 </table>
 ~~~

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@@ -126,4 +126,8 @@
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+    vertical-align: top;
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