PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Built by researchers, for research.

Source-to-Source Debuggable Derivatives in Pure Python

Aesara is a Python library for defining, optimizing, and efficiently evaluating mathematical expressions involving multi-dimensional arrays.

『ゼロから作る Deep Learning ❸』(O'Reilly Japan, 2020)

Optimal transport tools implemented with the JAX framework, to solve large scale matching problems of any flavor.

Introductions to key concepts in quantum programming, as well as tutorials and implementations from cutting-edge quantum computing research.

TorchOpt is an efficient library for differentiable optimization built upon PyTorch.

Differentiable Fluid Dynamics Package

Betty: an automatic differentiation library for generalized meta-learning and multilevel optimization

Tensor network based quantum software framework for the NISQ era

🔬 Nano size Theano LSTM module

Tensorlang, a differentiable programming language based on TensorFlow

A deep learning framework created from scratch with Python and NumPy

Numerical integration in arbitrary dimensions on the GPU using PyTorch / TF / JAX

adam implements a collection of algorithms for calculating rigid-body dynamics in Jax, CasADi, PyTorch, and Numpy.

A JIT compiler for hybrid quantum programs in PennyLane

PyTorch for Quantitative Finance : Payoffs are Activations

A differentiable physics engine and multibody dynamics library for control and robot learning.

An Automatic Differentiation-based Waveform Inversion Framework Implemented in PyTorch.

Differentiable Programming Tensor Networks