TensorNetwork: A Library for Physics and Machine Learning

Abstract

TensorNetwork is an open source library for implementing tensor network algorithms. Tensor networks are sparse data structures originally designed for simulating quantum many-body physics, but are currently also applied in a number of other research areas, including machine learning. We demonstrate the use of the API with applications both physics and machine learning, with details appearing in companion papers.

The TensorNetwork library can be found at https://github.com/google/tensornetwork

References in corpus (16)

• The density-matrix renormalization group
• Efficient classical simulation of slightly entangled quantum computations
• Entanglement renormalization
• Tensor-Entanglement-Filtering Renormalization Approach and Symmetry Protected Topological Order
• A class of quantum many-body states that can be efficiently simulated
• Tensor renormalization group approach to 2D classical lattice models
• Classical simulation of quantum many-body systems with a tree tensor network
• Renormalization algorithms for Quantum-Many Body Systems in two and higher dimensions
• Hand-waving and Interpretive Dance: An Introductory Course on Tensor Networks
• Tensor Network Renormalization
• Algorithms for entanglement renormalization
• Tensor product methods and entanglement optimization for ab initio quantum chemistry
• Continuous Matrix Product States for Quantum Fields
• Entanglement renormalization for quantum fields
• A Density Matrix Algorithm for 3D Classical Models
• Fermionic orbital optimisation in tensor network states

Cited by in corpus (31)

• PennyLane: Automatic differentiation of hybrid quantum-classical computations
• Beyond photon pairs: Nonlinear quantum photonics in the high-gain regime
• Supervised Learning with Projected Entangled Pair States
• Quantum Hamiltonian-Based Models and the Variational Quantum Thermalizer Algorithm
• DisCoPy: Monoidal Categories in Python
• Tree tensor network classifiers for machine learning: from quantum-inspired to quantum-assisted
• Tensor-based algorithms for image classification
• Lectures on Quantum Tensor Networks
• Anomaly Detection with Tensor Networks
• An introduction to infinite projected entangled-pair state methods for variational ground state simulations using automatic differentiation
• From Tree Tensor Network to Multiscale Entanglement Renormalization Ansatz
• TedNet: A Pytorch Toolkit for Tensor Decomposition Networks
• Recycling qubits in near-term quantum computers
• A High-Performance Sparse Tensor Algebra Compiler in Multi-Level IR
• Qsun: an open-source platform towards practical quantum machine learning applications
• Applications and resource reductions in measurement-based variational quantum eigensolvers
• Variational Autoencoder Analysis of Ising Model Statistical Distributions and Phase Transitions
• Adaptive Learning of Tensor Network Structures
• Continuous-Variable Entanglement through Central Forces: Application to Gravity between Quantum Masses
• Quantum-Classical Machine learning by Hybrid Tensor Networks
• Quantum Circuit Simulation with Fast Tensor Decision Diagram
• The landscape of software for tensor computations
• Accuracy and Efficiency of Simplified Tensor Network Codes
• Unifying O(3) Equivariant Neural Networks Design with Tensor-Network Formalism
• The Cytnx Library for Tensor Networks
• Tensor Network for Supervised Learning at Finite Temperature
• AutoHOOT: Automatic High-Order Optimization for Tensors
• Differentiate Everything with a Reversible Embeded Domain-Specific Language
• Quantum Machine Learning with SQUID
• TensorNetwork on TensorFlow: Entanglement Renormalization for quantum critical lattice models
• Block-term Tensor Neural Networks