Awesome machine learning for combinatorial optimization papers.

A library for easy and efficient manipulation of tensor networks.

Awesome resources on normalizing flows.

Riemannian Adaptive Optimization Methods with pytorch optim

DAGs with NO TEARS: Continuous Optimization for Structure Learning

Tensor Network Learning with PyTorch

TensorNet (TensorFlow implementation)

PyTorch toolbox for matrix product state models

Tensor-Train decomposition in pytorch

Riemannian optimization for quantum technologies

code for Unsupervised Generative Modeling using Matrix Product States

Official implementation of "Low-Rank Tensor Function Representation for Multi-Dimensional Data Recovery," IEEE TPAMI, 2023

FSNet: Feasibility-Seeking Neural Network for Constrained Optimization with Guarantees

Gradient-free optimization method for the multidimensional arrays and discretized multivariate functions based on the tensor train (TT) format.

Probabilistic modelling with tensor networks

Tensor Networks for Machine Learning

Bayesian tensor network

Code for the paper Normalizing Flows are Capable Models for RL

customizable neural network architecture based on Kolmogorov-Arnold Networks (KANs), utilizing TensorFlow

Minimal matrix-product state algorithms library.

A Python library to automate generating, parallelizing, and executing quantum programs.

Tensor Network Learning with PyTorch

Hierarchical Tucker for Black Box approximation and optimization

Reasoning over knowledge graphs using tensor-based methods

Pytorch Real-Time Image Classification with CNN

This is the python code for "Tangent-Space Gradient Optimization of Tensor Network for Machine Learning" at https://arxiv.org/abs/2001.04029.

Code for Exact Diagonalization of Quantum Many-Body Hamiltonians and Lattice (Abelian and Non-Abelian) Gauge Theories in D=1,2,3 spatial dimensions

Quantum Krylov algorithm for solving the lattice Schwinger model