Search Results for "automatic differentiation"

...But JAX also lets you just-in-time compile your own Python functions into XLA-optimized kernels using a one-function API, jit. Compilation and automatic differentiation can be composed arbitrarily, so you can express sophisticated algorithms and get maximal performance without leaving Python.

Tequila is an abstraction framework for (variational) quantum algorithms. It operates on abstract data structures allowing the formulation, combination, automatic differentiation and optimization of generalized objectives. Tequila can execute the underlying quantum expectation values on state-of-the-art simulators as well as on real quantum devices.

Paddle Quantum (量桨) is the world's first cloud-integrated quantum machine learning platform based on Baidu PaddlePaddle. It supports the building and training of quantum neural networks, making PaddlePaddle the first deep-learning framework in China. Paddle Quantum is feature-rich and easy to use. It provides comprehensive API documentation and tutorials help users get started right away. Paddle Quantum aims at establishing a bridge between artificial intelligence (AI) and quantum computing...

A symbolic framework for C++, Python and Octave implementing automatic differentiation by source code transformation in forward and reverse modes on sparse matrix-valued computational graphs.

Python automatic differentiation class for forward mode automatic differentiation using operator overloading and reimplemented math functions. Single and partial derivatives are supported. Supported operators: +, -, *, /, **, +=, -=, *=, /=, **= Available functions: sin, cos, tan, asin, acos, atan, sqrt, exp, log, log10, sinh, cosh, tanh, asinh, acosh, atanh See README file for usage examples.

ADOL-Py is a python extension to the ADOL-C automatic differentiation library.