Code for the article "Quantum Machine Learning Tensor Network States"
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Updated
Jun 12, 2024 - Jupyter Notebook
Code for the article "Quantum Machine Learning Tensor Network States"
Tensor network simulations for finite temperature, open quantum system dynamics
A Julia library for efficient tensor computations and tensor network calculations
Efficient parallel quantum chemistry DMRG in MPO formalism
DMRGPy is a Python library to compute quasi-one-dimensional spin chains and fermionic systems using matrix product states with DMRG as implemented in ITensor. Most of the computations can be performed both with DMRG and exact diagonalization for small systems, which allows one to benchmark the results.
This is a package for calculating FLUCTUATIONS of heat transfer in the Spin-Boson model using the Time Evolving Density matrices using Orthogonal Polynomial Algorithm (TEDOPA).
Discrete optimization in the tensor-network (specifically, MPS-MPO) language.
Tensor network based quantum software framework for the NISQ era
Matrix Product Belief Propagation
Tensor Trains, mostly thought of as probability distributions
Open quantum system dynamics using process tensor networks and iTEBD.
A Python package for numerical quantum mechanics of chain-like systems based on tensor trains
A Tensor Network Library (TenNetLib.jl) built on top of ITensors.jl for quantum many-body problems.
Entanglement characterization of variational quantum circuits using a Matrix Product State simulator and qiskit.
C++17 toolkit to study the static properties of discrete quantum systems.
A library for easy and efficient manipulation of tensor networks.
Gradient Descent Optimization of MPS for Ground State Finding
This is a repository containing julia codes for MPS/MPDO based tensor network for many body problems (Ground State properties, Time evolution, and Thermal properties).
This repo contains an implementation of the Simple-Update Tensor Network algorithm as described in the paper - A universal tensor network algorithm for any infinite lattice by Saeed S. Jahromi and Roman Orus.
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