The Cytnx Library for Tensor Networks

TL;DR

Cytnx is a versatile tensor network library supporting both C++ and Python, optimized for physics simulations, with features like symmetry implementation, automatic contraction ordering, and GPU acceleration.

Contribution

It introduces a user-friendly, cross-language tensor network library with symmetry support and automatic contraction optimization, enhancing efficiency for classical and quantum physics simulations.

Findings

Efficient tensor operations on CPU and GPU demonstrated

User-friendly interface similar to NumPy and PyTorch

Supports multiple Abelian symmetries and automatic contraction ordering

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.