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Tensor Computing Interface: An Application-Oriented, Lightweight Interface for Portable High-Performance Tensor Network Applications

arXiv 2512.23917 ↗First posted 30 Dec 2025quant-ph cond-mat.str-el cs.LGcited by 0

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Abstract (arXiv)

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 textitCytnx, demonstrating its practicality and ease of integration with existing tensor-computing frameworks.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 77 references that are in the map. First posted on arXiv 30 Dec 2025.

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References · 22

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2017Tensor Fusion Network for Multimodal Sentiment Analysiscited by 433
  3. 2015Tensorizing Neural Networkscited by 250
  4. 2019A Tensorized Transformer for Language Modelingcited by 86
  5. 2018Towards Quantum Machine Learning with Tensor Networkscited by 76
  6. 2016Deep Multi-task Representation Learning: A Tensor Factorisation Approachcited by 69
  7. 2016Ultimate tensorization: compressing convolutional and FC layers alikecited by 56
  8. 2017Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectivescited by 54
  9. 2019Factorized Higher-Order CNNs with an Application to Spatio-Temporal Emotion Estimationcited by 40
  10. 2020An Optimal Statistical and Computational Framework for Generalized Tensor Estimationcited by 39
  11. 2023Tensor Networks Meet Neural Networks: A Survey and Future Perspectivescited by 32
  12. 2024QuanTA: Efficient High-Rank Fine-Tuning of LLMs with Quantum-Informed Tensor Adaptationcited by 23
10 more
  1. 2023Tensor networks for quantum machine learningcited by 17
  2. 2023TensorGPT: Efficient Compression of Large Language Models based on Tensor-Train Decompositioncited by 17
  3. 2022TTOpt: A Maximum Volume Quantized Tensor Train-based Optimization and its Application to Reinforcement Learningcited by 15
  4. 2024CoMERA: Computing- and Memory-Efficient Training via Rank-Adaptive Tensor Optimizationcited by 15
  5. 2024Tensor Train Low-rank Approximation (TT-LoRA): Democratizing AI with Accelerated LLMscited by 14
  6. 2023Efficient GPT Model Pre-training using Tensor Train Matrix Representationcited by 8
  7. 2018A Quantum Many-body Wave Function Inspired Language Modeling Approachcited by 7
  8. 2025Transformed Low-rank Adaptation via Tensor Decomposition and Its Applications to Text-to-image Modelscited by 3
  9. 2025Tensor Network Based Feature Learning Modelcited by 3
  10. 2022Validating quantum-supremacy experiments with exact and fast tensor network contractioncited by 1

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