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Tensor computing application programming interface: An application-oriented, lightweight API for portable high-performance tensor network computations

Rong-Yang Sun1,2,3, Tomonori Shirakawa3,4,2,5,6, Hidehiko Kohshiro3,4,7, D. N. Sheng1, and Seiji Yunoki3,4,5,8

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APS Open Sci. 1, 000214 – Published 13 November, 2026

DOI: https://doi.org/10.1103/dmx5-95ny

Abstract

Tensor networks (TNs) are a central computational tool in quantum science and artificial intelligence. However, the lack of a unified software interface across tensor-computing frameworks severely limits the portability of TN applications, coupling algorithmic development to specific hardware and software backends. To address this challenge, we introduce the tensor computing application programming interface (TCAPI)—an application-oriented, lightweight API designed to enable framework-independent, high-performance TN applications, currently specified for C++ and Python. TCAPI provides well-defined type-system conventions that abstract tensor objects, together with a minimal yet expressive set of core functions for essential tensor manipulations and tensor linear-algebra operations, with operation semantics shared across language specifications. Through numerical demonstrations of representative tensor-network applications implemented in C++ using TCAPI, we show that code written against TCAPI can be migrated seamlessly across heterogeneous hardware and software platforms while achieving performance comparable to native framework implementations. We further provide open-source implementations of TCAPI based on Cytnx and ITensor in C++, and on NumPy in Python, demonstrating its practicality and ease of integration with existing tensor-computing frameworks.

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APS Open Sci. 1, 000214– Published 13 November, 2026
Vol. 1
  • Received 7 February 2026
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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