Tensor Network Python (TeNPy) version 1

Abstract

TeNPy (short for 'Tensor Network Python') is a python library for the simulation of strongly correlated quantum systems with tensor networks. The philosophy of this library is to achieve a balance of readability and usability for new-comers, while at the same time providing powerful algorithms for experts. The focus is on MPS algorithms for 1D and 2D lattices, such as DMRG ground state search, as well as dynamics using TEBD, TDVP, or MPO evolution. This article is a companion to the recent version 1.0 release of TeNPy and gives a brief overview of the package.

v3: published version with small additional clarifications suggested by referees

References in corpus (8)

• The density-matrix renormalization group in the age of matrix product states
• Computational complexity and fundamental limitations to fermionic quantum Monte Carlo simulations
• Time-evolving a matrix product state with long-ranged interactions
• Scaling of entanglement support for Matrix Product States
• Tensor network states and algorithms in the presence of a global U(1) symmetry
• Applying matrix product operators to model systems with long-range interactions
• Finite automata for caching in matrix product algorithms
• Automated construction of $U(1)$-invariant matrix-product operators from graph representations

Cited by in corpus (33)

• Anyon Superconductivity from Topological Criticality in a Hofstadter-Hubbard Model
• Strong and weak symmetries and their spontaneous symmetry breaking in mixed states emerging from the quantum Ising model under multiple decoherence
• Fractional Chern Insulator and Quantum Anomalous Hall Crystal in Twisted MoTe${}_2$
• Deconfined quantum criticality of frustrated hard-core dipolar bosons
• Chiral Spin Liquid and Quantum Phase Transition in the Triangular Lattice Hofstadter-Hubbard Model
• Renormalized dual basis for scalable simulations of 2+1D compact quantum electrodynamics
• Quantum spin models of commensurate $p$-wave magnets
• A Practical Introduction to Tensor Network Renormalization with TNRKit.jl
• Pseudogap with Fermi arcs and Fermi pockets in half-filled twisted transition metal dichalcogenides
• Highly-entangled, highly-doped states that are efficiently cross-device verifiable
• Dualities between 2+1d fusion surface models from braided fusion categories
• Topological order in symmetric blockade structures
• Symmetry-deformed toric codes and the quantum dimer model
• Subexponential decay of local correlations from diffusion-limited dephasing
• Signatures of superconducting pairing driven by electron-electron interactions in moiré WSe${}_2$/WSe${}_2$ homobilayer modelled by Hubbard Hamiltonian
• Seeding neural network quantum states with tensor network states
• Diagnosing energy gap in quantum spin liquids via polarization amplitude
• Scalable Effective Models for Superconducting Nanostructures: Applications to Double, Triple, and Quadruple Quantum Dots
• The Software Landscape for the Density Matrix Renormalization Group
• Spin-orbital magnetism in moiré Wigner molecules
• Boundary Conditions for the Entanglement Cut in 2D Conformal Field Theories
• Realization of Two-dimensional Discrete Time Crystals with Anisotropic Heisenberg Coupling
• Stabilization of Granovskii-Zhedanov scars of the XYZ quantum spin chain via non-Hermitian spin relaxation
• Shortcuts to Analog Preparation of Non-Equilibrium Quantum Lakes
• Evaluating Sample-Based Krylov Quantum Diagonalization for Heisenberg Models with Applications to Materials Science
• Unconventional Spin Dynamics and Supersolid Excitations in the Triangular-Lattice XXZ Model
• Beyond fragmented dopant dynamics in quantum spin lattices: Robust localization and non-Gaussian diffusion
• Energy Spectra of Compressed Quantum States
• Chiral Edge Excitations of $\nu =1/2$ Fractional Chern Insulators in the Bosonic Hofstadter Model
• Two-dimensional many-body localized systems coupled to a heat bath
• Exploring the performance of superposition of product states: from 1D to 3D quantum spin systems
• Unveiling hidden features of the Kitaev model through a complex-network analysis
• Dominant Excitonic Superconductivity in a Three-component Hubbard Chain