The Cytnx Library for Tensor Networks

2024-01-03

arXiv.org

AI Summary

To address the high development barrier, poor cross-platform compatibility, inefficient symmetry handling, and suboptimal contraction optimization in tensor network algorithms, this paper introduces Cytnx—an open-source library. Cytnx features a unified C++/Python dual-backend interface with syntax compatibility to NumPy and PyTorch. It proposes a novel dynamic-graph-based automatic contraction scheduler built upon a *Network* abstraction, enabling native support for mixed storage and efficient computation with multiple global Abelian symmetries. Furthermore, it integrates NVIDIA cuQuantum for GPU acceleration. Experimental results demonstrate that Cytnx significantly outperforms manual contraction schemes on both CPU and GPU platforms, drastically reducing implementation complexity. As a result, Cytnx provides a high-performance, user-friendly, and extensible tensor network computing infrastructure for classical and quantum many-body physics simulations.

Technology Category

• Machine Learning: Matrix & Tensor Methods
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Social Networks and Social Media: Computational social science
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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.

Problem

• Tensor Network
• High-performance Computing
• Quantum Physics

Innovation

• Tensor Network Library
• GPU Acceleration
• Symmetry Optimization