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.

References in corpus (44)

• The density-matrix renormalization group in the age of matrix product states
• A Practical Introduction to Tensor Networks: Matrix Product States and Projected Entangled Pair States
• The ITensor Software Library for Tensor Network Calculations
• Matrix Product States and Projected Entangled Pair States: Concepts, Symmetries, and Theorems
• Unifying time evolution and optimization with matrix product states
• Tensor networks for complex quantum systems
• Renormalization algorithms for Quantum-Many Body Systems in two and higher dimensions
• Simulating Lattice Gauge Theories within Quantum Technologies
• From density-matrix renormalization group to matrix product states
• Efficient numerical simulations with Tensor Networks: Tensor Network Python (TeNPy)
• Matrix product operator representations
• The Density Matrix Renormalization Group in Chemistry and Molecular Physics: Recent Developments and New Challenges
• Simulation of strongly correlated fermions in two spatial dimensions with fermionic Projected Entangled-Pair States
• Tensor network states and algorithms in the presence of a global U(1) symmetry
• Tensor network decompositions in the presence of a global symmetry
• Lattice gauge theories simulations in the quantum information era
• Non-abelian symmetries in tensor networks: a quantum symmetry space approach
• Simulation methods for open quantum many-body systems
• Differentiable Programming Tensor Networks
• Review on novel methods for lattice gauge theories
• Fermionic multi-scale entanglement renormalization ansatz
• Efficient tensor network simulation of IBM's Eagle kicked Ising experiment
• The Tensor Networks Anthology: Simulation techniques for many-body quantum lattice systems
• Faster Methods for Contracting Infinite 2D Tensor Networks
• Tensor network states and algorithms in the presence of a global SU(2) symmetry
• Variational Quantum Reinforcement Learning via Evolutionary Optimization
• Faster identification of optimal contraction sequences for tensor networks
• Tensor lattice field theory with applications to the renormalization group and quantum computing
• TensorNetwork: A Library for Physics and Machine Learning
• X-Symbols for Non-Abelian Symmetries in Tensor Networks
• Investigation of the Néel phase of the frustrated Heisenberg antiferromagnet by differentiable symmetric tensor networks
• Automatic differentiation applied to excitations with Projected Entangled Pair States
• Efficient simulation of Grassmann Tensor Product States
• Complete quantum-inspired framework for computational fluid dynamics
• Tensor networks for unsupervised machine learning
• Tensor network reduced order models for wall-bounded flows
• NCON: A tensor network contractor for MATLAB
• An introduction to infinite projected entangled-pair state methods for variational ground state simulations using automatic differentiation
• TeNeS: Tensor Network Solver for Quantum Lattice Systems
• Simulating both parity sectors of the Hubbard Model with Tensor Networks
• Fast classical simulation of evidence for the utility of quantum computing before fault tolerance
• Stable and efficient differentiation of tensor network algorithms
• GrassmannTN: a Python package for Grassmann tensor network computations
• Simulation of IBM's kicked Ising experiment with Projected Entangled Pair Operator

Cited by in corpus (1)

• The Software Landscape for the Density Matrix Renormalization Group