High-performance automatic differentiation of LLVM and MLIR.
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Updated
May 15, 2024 - LLVM
High-performance automatic differentiation of LLVM and MLIR.
Julia bindings for the Enzyme automatic differentiator
This PennyLane plugin allows the Rigetti Forest QPUs, QVM, and wavefunction simulator to optimize quantum circuits.
A C++ implementation of an OFDFT based molecular force field model.
PennyLane is a cross-platform Python library for differentiable programming of quantum computers. Train a quantum computer the same way as a neural network.
A JIT compiler for hybrid quantum programs in PennyLane
An interface to various automatic differentiation backends in Julia.
Tensor library for machine learning
AD-backend agnostic system defining custom forward and reverse mode rules. This is the light weight core to allow you to define rules for your functions in your packages, without depending on any particular AD system.
Thermodynamic Equations of State, Fortran library with both automatic and anallytical derivation capabilities
Machine Learning Algorithms in Fortran
A numerical and automatic mathematical library in C++ for scientific and graphical applications.
Repository for automatic differentiation backend types
Math on (Hyper-Dual) Tensors with Trailing Axes
Extension of DOLFINx implementing the concept of external operator
Automatic Differentiation + Adjoint + Shocks Experiments
Introductions to key concepts in quantum programming, as well as tutorials and implementations from cutting-edge quantum computing research.
R package for score matching by automatic differentiation
Parallel Reverse Mode Automatic Differentiation in C# for Custom Neural Network Development
Julia interface to the Generalised Truncated Power Series Algebra (GTPSA) library in MAD
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