Tensor network methods are a conceptually elegant framework for encoding complicated datasets, where high-order tensors are approximated as networks of low-order tensors. In practice, however, the numeric implementation of tensor network algorithms is often a labor-intensive and error-prone task, even for experienced researchers in this area. \emph{TensorTrace} is application designed to alleviate the burden of contracting tensor networks: it provides a graphic drawing interface specifically tailored for the construction of tensor network diagrams, from which the code for their optimal contraction can then be automatically generated (in the users choice of the MATLAB, Python or Julia languages). \emph{TensorTrace} is freely available at \url{https://www.tensortrace.com} with versions for Windows, Mac and Ubuntu.
翻译:Tensor 网络方法是一个概念上优雅的编码复杂数据集框架,高阶高压电解器可被近似于低阶电解器网络。然而,在实践中,即使对该领域有经验的研究人员来说,高压网络算法的数位实施往往也是一项劳动密集型和容易出错的任务。 \emph{TensorTrace}是旨在减轻签订拖网负担的应用工具:它提供了一个图形绘图界面,专门设计用于构建长线网络图,然后可以自动生成其最佳收缩代码(根据MATLAB、Python或Julia语言的用户选择)。\emph{TensorTrace}可在以下网站免费获得:https://www.tensortrace.com},其版本为Windows、Mac和Ubuntu。