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.
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Updated
Jun 12, 2024 - Python
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.
A JIT compiler for hybrid quantum programs in PennyLane
Automatic differentiation of implicit functions
High-performance automatic differentiation of LLVM and MLIR.
A differentiable physics engine and multibody dynamics library for control and robot learning.
Tensor library for machine learning
Comprehensive automatic differentiation in C++
adam implements a collection of algorithms for calculating rigid-body dynamics in Jax, CasADi, PyTorch, and Numpy.
A Julia interface to the C++ library ColPack for graph and sparse matrix coloring.
PotentialLearning.jl: Composable Optimization Workflows for Fast and Accurate Interatomic Potentials.
Julia bindings for the Enzyme automatic differentiator
A minimal OpenCL, CUDA, Vulkan and host CPU array manipulation engine / framework.
JAX compilation of RDDL description files, and a differentiable planner in JAX.
Introductions to key concepts in quantum programming, as well as tutorials and implementations from cutting-edge quantum computing research.
A numerical and automatic mathematical library in C++ for scientific and graphical applications.
⟨Grassmann-Clifford-Hodge⟩ multilinear differential geometric algebra
An interface to various automatic differentiation backends in Julia.
Source Code Generation for Automatic Differentiation using Operator Overloading
PyTorch Autodiff DFT-D4 Implementation.
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