Machine learning spatial geometry from entanglement features

Yi-Zhuang You; Zhao Yang; Xiao-Liang Qi

首页> 外文期刊>Physical review >Machine learning spatial geometry from entanglement features

【24h】

Machine learning spatial geometry from entanglement features

机译：从纠缠特征机器学习空间几何

获取原文

获取原文并翻译 | 示例

掌桥外文数据库（机构版） >>

开具论文收录证明 >>

文献代查 >>

页面导航

摘要
著录项
相似文献
相关主题

摘要

Motivated by the close relations of the renormalization group with both the holography duality and the deep learning, we propose that the holographic geometry can emerge from deep learning the entanglement feature of a quantum many-body state. We develop a concrete algorithm, call the entanglement feature learning (EFL), based on the random tensor network (RTN) model for the tensor network holography. We show that each RTN can be mapped to a Boltzmann machine, trained by the entanglement entropies over all subregions of a given quantum many-body state. The goal is to construct the optimal RTN that best reproduce the entanglement feature. The RTN geometry can then be interpreted as the emergent holographic geometry. We demonstrate the EFL algorithm on a 1D free fermion system and observe the emergence of the hyperbolic geometry (AdS_3 spatial geometry) as we tune the fermion system towards the gapless critical point (CFT_2 point).

机译：归因于归一化组与全息对偶性和深度学习的紧密关系，我们提出全息几何可以从深度学习中显现出量子多体态的纠缠特征。我们基于张量网络全息的随机张量网络（RTN）模型，开发了一种具体的算法，称为纠缠特征学习（EFL）。我们表明，每个RTN可以映射到一个给定的量子多体态的所有子区域上的纠缠熵训练的Boltzmann机。目的是构建最能重现纠缠特征的最佳RTN。然后可以将RTN几何图形解释为紧急全息图形。我们在一维自由费米子系统上演示了EFL算法，并在朝向无间隙临界点（CFT_2点）调整费米子系统时观察到了双曲线几何（AdS_3空间几何）的出现。

著录项

来源
《Physical review》 |2018年第4期|045153.1-045153.13|共13页
作者
Yi-Zhuang You; Zhao Yang; Xiao-Liang Qi;
展开▼
作者单位

Department of Physics, Harvard University, Cambridge, Massachusetts 02138, USA;

Department of Physics, Stanford University, California 94305, USA;

Department of Physics, Stanford University, California 94305, USA;

展开▼
收录信息
原文格式 PDF
正文语种 eng
中图分类
关键词

相似文献

外文文献
中文文献
专利

1. Machine learning spatial geometry from entanglement features [J] . You Yi-Zhuang, Yang Zhao, Qi Xiao-Liang Physical review, B . 2018,第4期

机译：从纠缠功能的机器学习空间几何
2. A new machine learning based geometry feature extraction approach for energy consumption estimation in mask image projection stereolithography [J] . Yiran Yang, Miao He, Lin Li Procedia CIRP . 2019,第5期

机译：一种新的基于机器学习的几何特征提取方法，用于掩模图像投影立体光刻中的能耗估算
3. Machine learning -based prediction of the shale barrier size and spatial location using key features of SAGD production curves [J] . Kim Min, Shin Hyundon Journal of Petroleum Science & Engineering . 2020,第1期

机译：使用SAGD生产曲线的关键特征 - 基于机器学习 - 基于页岩屏障尺寸和空间位置的预测
4. Fingerprint Liveness Detection with Machine Learning Classifiers using Feature Level Fusion of Spatial and Transform Domain Features [C] . Smita Khade, Sudeep D. Thepade International Conference on Computing, Communication, Control and Automation . 2019

机译：使用机器学习分类器的指纹活度检测，使用空间和变换域特征的特征级融合
5. Iterative feature weighting for identification of relevant features in machine learning: With multilayer perceptron, radial basis function and support vector architectures. [D] . Duan, Baofu. 2005

机译：用于识别机器学习中相关特征的迭代特征加权：具有多层感知器，径向基函数和支持向量体系结构。
6. Tensor network simulation of multi-environmental open quantum dynamics via machine learning and entanglement renormalisation [O] . Florian A. Y. N. Schröder, David H. P. Turban, Andrew J. Musser, -1

机译：通过机器学习和纠缠重归一化进行多环境开放量子动力学的Tensor网络仿真
7. Machine Learning Spatial Geometry from Entanglement Features [O] . You, Yi-Zhuang, Yang, Zhao, Qi, Xiao-Liang 2017

机译：基于纠缠特征的机器学习空间几何

Machine learning spatial geometry from entanglement features

摘要

著录项

相似文献

相关主题

期刊订阅