为张量网络应用设计轻量级接口，实现跨平台高性能计算。
Tensor Computing Interface: An Application-Oriented, Lightweight Interface for Portable High-Performance Tensor Network Applications
- 定义统一张量类型系统与核心操作接口
- 代码可在异构平台间无缝迁移且性能接近原生
- 适配现有框架，适合需跨平台部署的科研开发者
张量网络（TNs）是量子科学和人工智能中的核心计算工具。然而，不同张量计算框架之间缺乏统一接口，严重限制了张量网络应用的可移植性，导致算法开发被绑定于特定硬件与软件后端。为此，我们提出张量计算接口（TCI）——一种面向应用、轻量级的应用编程接口，旨在实现框架无关的高性能张量网络应用。TCI 提供明确定义的类型系统，抽象张量对象，并包含一组最小但表达力强的核心函数，覆盖关键的张量操作与张量线性代数运算。通过在典型张量网络应用上的数值验证，我们表明基于 TCI 编写的代码可在异构软硬件平台间无缝迁移，性能媲美原生框架实现。我们进一步基于 extit{Cytnx} 发布了 TCI 的开源实现，证明其实际可用性与对现有张量计算框架的易集成性。
原文摘要 · Abstract (English)
Tensor networks (TNs) are a central computational tool in quantum science and artificial intelligence. However, the lack of unified software interface across tensor-computing frameworks severely limits the portability of TN applications, coupling algorithmic development to specific hardware and software back ends. To address this challenge, we introduce the Tensor Computing Interface (TCI) -- an application-oriented, lightweight application programming interface designed to enable framework-independent, high-performance TN applications. TCI provides a well-defined type system that abstracts tensor objects together with a minimal yet expressive set of core functions covering essential tensor manipulations and tensor linear-algebra operations. Through numerical demonstrations on representative tensor-network applications, we show that codes written against TCI can be migrated seamlessly across heterogeneous hardware and software platforms while achieving performance comparable to native framework implementations. We further release an open-source implementation of TCI based on \textit{Cytnx}, demonstrating its practicality and ease of integration with existing tensor-computing frameworks.
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