张量网络计算,捕捉严格的变性,体积定律行为,以及神经网络状态的高效表示
Wen-Yuan Liu1, Si-Jing Du2, Ruojing Peng1
1California Institute of Technology, Division of Chemistry and Chemical Engineering, Pasadena, California 91125, USA.
Physical review letters
|January 29, 2025
概括
我们介绍了张量网络函数,一种新的状态类,可以克服传统张量网络的计算限制. 这允许精确的能量估计和更广泛的应用在物理学和机器学习.
科学领域:
- 量子多体物理学 量子多体物理学
- 计算物理 计算物理
- 机器学习 机器学习
背景情况:
- 张量网络状态是模拟量子系统的强大工具.
- 传统的张量网络收缩面临着计算限制,特别是在循环图上.
- 大致收缩方法可能会限制准确性和适用性.
研究的目的:
- 介绍了一个新的视角,张量网络状态称为张量网络函数.
- 删除与近似张量网络收缩相关的计算限制.
- 将张量网络的适用性扩展到具有复杂计算图形的系统.
主要方法:
- 根据广度收缩的计算图定义张量网络函数.
- 在循环图上使用张量网络函数进行严格的变量能量的估计.
- 将一般的前神经网络映射到高效的张量网络函数上.
主要成果:
- 张量网络函数在没有计算限制的情况下继承张量网络状态的优势.
- 在循环图表上展示精确的变化能量的估计.
- 分析基本状态的表达力,并捕捉体积定律时间演变的方面.
结论:
- 张量网络函数扩大了可计算的张量网络的范围.
- 在传统方法失败的情况下,启用精确的张量网络计算.
- 在张量网络,量子物理学和机器学习的交叉点开辟新的研究途径.
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