A JIT compiler for hybrid quantum programs in PennyLane
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Updated
Jun 5, 2024 - Python
A JIT compiler for hybrid quantum programs in PennyLane
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.
Сustom torch style machine learning framework with automatic differentiation implemented on numpy, allows build GANs, VAEs, etc.
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.
TorchOpt is an efficient library for differentiable optimization built upon PyTorch.
Optimal transport tools implemented with the JAX framework, to get differentiable, parallel and jit-able computations.
Links to Fortran compilers, preprocessors, static analyzers, transpilers, IDEs, build systems, etc.
QuantumFlow: A Quantum Algorithms Development Toolkit
A differentiable physics engine and multibody dynamics library for control and robot learning.
A numerical and automatic mathematical library in C++ for scientific and graphical applications.
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.
Comprehensive automatic differentiation in C++
Tensor library for machine learning
The Stan Math Library is a C++ template library for automatic differentiation of any order using forward, reverse, and mixed modes. It includes a range of built-in functions for probabilistic modeling, linear algebra, and equation solving.
A fast and flexible implementation of Rigid Body Dynamics algorithms and their analytical derivatives
M.I.T General Circulation Model master code and documentation repository
One More Einsum for Julia! With runtime order-specification and high-level adjoints for AD
Tensor network based quantum software framework for the NISQ era
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