Tensor network based quantum software framework for the NISQ era
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Updated
May 30, 2024 - Python
Tensor network based quantum software framework for the NISQ era
R package for score matching by automatic differentiation
An interface to various automatic differentiation backends in Julia.
This repo hosts the notes and tutorials related to natural language processing in the format of blogging.
PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Train a quantum computer the same way as a neural network.
Math on (Hyper-Dual) Tensors with Trailing Axes
A fast and flexible implementation of Rigid Body Dynamics algorithms and their analytical derivatives
A JIT compiler for hybrid quantum programs in PennyLane
Small autodiff lib and a simple working feedforward neural net in Haskell on top of it, from scratch, zero-deps.
Julia bindings for the Enzyme automatic differentiator
Introductions to key concepts in quantum programming, as well as tutorials and implementations from cutting-edge quantum computing research.
Some basic examples to get started with Mathematica+AceGen to build functions, material models, element formulations for Matlab/Fortran/C++
ODE integration using Taylor's method, and more, in Julia
Tensor library for machine learning
A fast, ergonomic and portable tensor library in Nim with a deep learning focus for CPU, GPU and embedded devices via OpenMP, Cuda and OpenCL backends
Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.
automatic differentiation made easier for C++
Automatic differentiation of implicit functions
Forward Mode Automatic Differentiation for Julia
Taylor-mode automatic differentiation for higher-order derivatives
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