The Cytnx Library for Tensor Networks
Abstract
We introduce a tensor network library designed for classical and quantum physics simulations called Cytnx (pronounced as sci-tens). This library provides almost an identical interface and syntax for both C++ and Python, allowing users to effortlessly switch between two languages. Aiming at a quick learning process for new users of tensor network algorithms, the interfaces resemble the popular Python scientific libraries like NumPy, Scipy, and PyTorch. Not only multiple global Abelian symmetries can be easily defined and implemented, Cytnx also provides a new tool called Network that allows users to store large tensor networks and perform tensor network contractions in an optimal order automatically. With the integration of cuQuantum, tensor calculations can also be executed efficiently on GPUs. We present benchmark results for tensor operations on both devices, CPU and GPU. We also discuss features and higher-level interfaces to be added in the future.
Keywords
Cite
@article{arxiv.2401.01921,
title = {The Cytnx Library for Tensor Networks},
author = {Kai-Hsin Wu and Chang-Teng Lin and Ke Hsu and Hao-Ti Hung and Manuel Schneider and Chia-Min Chung and Ying-Jer Kao and Pochung Chen},
journal= {arXiv preprint arXiv:2401.01921},
year = {2025}
}