+++ title = "JuliaCN Meetup 2024" tags = ["2024", "juliacn", "meetup", "winter", "见面会", "冬季", "program", "日程"] +++
- 时间：2024 年 11 月 1 日 (周五，注册日) - 11 月 3 日
- 地点：广州市南沙区东涌镇笃学路1号，香港科技大学（广州）实验楼W4, 1楼, 101.
报名已经截止。
- Bilibili 直播间：JuliaLang中文社区
- Zoom ID: 968 9626 6208, Password: 666
- Zoom link:https://hkust-gz-edu-cn.zoom.us/j/96896266208?pwd=coobxF7k44M5oVNnB0E0LDa0SPVz1X.1
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<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>
</tr>
<tr>
<td>9:00PM-9:40PM</td>
<td title="High performance computing is usually talked about in the domain of numerical methods: numerically solving PDEs, numerically solving optimization problems, etc. However, the real great sages of performance have always noted that they do something a little different in their domain. In computational fluid dynamics, split solvers can solve for the pressure separately from the velocity, while in robotics many small tricks in the problem formulation improve numerical integration performance by orders of magnitude. These tricks are always regarded as the reason why such domains cannot use general methods, but is that really the case? In this talk we will introduce symbolic-numerics, a form of numerical methods which weave in symbolic manipulation in order to allow for more performance than the numerics on its own. We will showcase the many symbolic-numeric algorithms already possible in the Julia SciML ecosystem, and some of the methods soon to be released, and show how many of the domain-specific tricks that people currently do by hand can be automated in a symbolic-numeric system.">Symbolic-numerics. New methods we have that do better than traditional numerical solvers, <strong>Chris Rackauckas</strong>, ResearchAffiliate (Co-PI of the Julia Lab)，MIT (在线)</td>
</tr>
<tr>
<td>10:00PM-10:40PM</td>
<td title="大型张量网络收缩技术在量子线路模拟，组合优化以及概率推断中的应用。">大型张量网络收缩技术及其应用, <strong>刘金国</strong>, 助理教授，香港科技大学（广州） (在线)</td>
</tr>
</table>
<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>
</tr>
<tr style="border-bottom: 1px solid #000;">
<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 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>
<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>
</tr>
<tr>
<td>4:00PM-4:40PM </td>
<td title="TreeWidthSolver.jl 是一个用于搜索简单图树宽度的 Julia 包。树宽度是图论中的一个概念，用于描述图的结构复杂性。树宽度越小，图的结构越简单。在张量网络中，树宽度决定了张量网络收缩的顺序，从而影响计算的效率和精度。TreeWidthSolver.jl 提供了一种高效的算法来搜索树宽度，并将其应用于张量网络的收缩顺序优化。报告将介绍树宽度与张量网络收缩顺序的关系，并展示如何使用 TreeWidthSolver.jl 进行张量网络收缩顺序优化。">TreeWidthSolver.jl: 从树宽度到张量网络收缩顺序, <strong>高煊钊</strong>，在读博士生，香港科技大学（广州）功能枢纽，先进材料学域</td>
</tr>
</table>
<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>
</tr>
<tr style="border-bottom: 1px solid #000;">
<td>11:00AM-11:40AM </td>
<td title="Quantum error-correcting codes (QECCs) are key techniques for overcoming noise in quantum computers. In this talk， I will introduce my work for QuantumClifford.jl: (1) The construction and evaluation of QECCs， including quantum low-density parity-check codes; (2) Decoders for these codes， including the BP-OSD decoder. The work is a project in Google Summer of Codes 2024.">QuantumClifford.jl 中的纠错码, <strong>鄢语轩</strong>，研究生，清华大学</td>
</tr>
<tr>
<td>2:00PM-6:00PM </td>
<td>Hackathon (<a href="https://github.com/JuliaCN/meetup-website/issues/8">Issue list</a>)</td>
</tr>
</table>
- 通过在以下链接下评论发布你的提案：#8 .
- 在黑客松开始时，做一个2-5分钟的简短介绍（可以有也可以没有幻灯片），目的是吸引其他人加入你的小组。如果你是打算发起一个大项目，请同时制定一个一个下午能够完成的目标。
- 合作完成你的提案。
- 在黑客松结束时，我们会留30分钟用于分享各组的成果。
地点为香港科技大学广州校区，位于广东省广州市南沙区东涌镇笃学路1号。如何报备入校请参考学校相关页面。承包参会者住宿，但仅为报告人报销路费（报销上限为2000），请保留好单据。 校园地图如下。
<div>
<a href="/assets/map.jpg" class="nounderline"><img src="/assets/map.jpg"/></a>
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- 食堂位于上图标记的C-6楼。
- 星巴克位于上图标记的C-1楼一楼。
- 活动教室位于W-4, 1楼, 101。
- 赵昱圣 (联络人: [email protected]), 高煊钊, 干则成, 朱国毅, 陈久宁, 田俊, 刘金国
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