Search Results for "automatic differentiation"
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Showing 14 open source projects for "automatic differentiation"
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Yao
Extensible, Efficient Quantum Algorithm Design for Humans
An intermediate representation to construct and manipulate your quantum circuit and let you make own abstractions on the quantum circuit in native Julia. Yao supports both forward-mode (faithful gradient) and reverse-mode automatic differentiation with its builtin engine optimized specifically for quantum circuits. Top performance for quantum circuit simulations. Its CUDA backend and batched quantum register support can make typical quantum circuits even faster. Yao is designed to be extensible. Its hierarchical architecture allows you to extend the framework to support and share your new algorithm and hardware. -
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JAX
Composable transformations of Python+NumPy programs
...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. -
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Tequila
A High-Level Abstraction Framework for Quantum Algorithms
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. -
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REDUCE
A Portable General-Purpose Computer Algebra System
REDUCE is an interactive system for general algebraic computations of interest to mathematicians, scientists and engineers. It can be used interactively for simple calculations but also provides a flexible and expressive user programming language. The development of the REDUCE computer algebra system was started in the 1960s by Anthony C. Hearn. Since then, many scientists from all over the world have contributed to its development. REDUCE has a long and distinguished place in the... -
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Paddle Quantum
Paddle Quantum
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... -
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AdaAutoDiff
C++ Templates and Ada Package for Automatic Differentiation
Operators are overloaded so that a normal looking function definition provides access to not only evaluations of itself, but to evaluations of any of its analytic derivatives. Automatic differentiation means the user does not need to define the analytic expressions for all the various partial derivatives. It also means that those complex expressions are essentially calculated at compile time, and merely evaluated at runtime. First order derivatives only, forward accumulation. Choose "files" and either the "initial_submission" directory for the Ada version, or the other directory "cpp_AD" for the C++ version. -
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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.
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ADiGator
A MATLAB Automatic Differentiation Tool
ADiGator is a source transformation via operator overloading tool for the automatic differentiation of mathematical functions written in MATLAB. Given a user written file, together with information on the inputs of said file, ADiGator uses forward mode automatic differentiation to generate a new file which contains the calculations required to compute the numeric derivatives of the original user function. Furthermore, these calculations are written entirely in the native MATLAB language, and thus the process may be repeated to obtain nth order derivative files. ... -
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OpenOCL Matlab
Optimal control, trajectory optimization, model-predictive control.
The Open Optimal Control Library is a software framework in Matlab/Octave for modeling optimal control problem. It uses automatic differentiation and fast non-linear programming solvers. It implements direct methods. In the backend it uses CasADi and ipopt. -
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Chebfun
Chebfun: numerical computing with functions
Chebfun is a MATLAB-based system for numerical computing with functions instead of just numbers. It represents functions using Chebyshev polynomial approximations and allows users to perform operations like differentiation, integration, root finding, and solving ODEs using symbolic-like syntax. Chebfun simplifies working with continuous mathematics using high-accuracy numerical techniques. -
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autodiff
Python class for automatic differentiation
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. -
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TIDES
Taylor series Integrator for Differential Equations
Taylor series Integrator for Differential EquationS. This software is developed by Profs. A. Abad, R. Barrio, F. Blesa and M. Rodriguez, (GME, University of Zaragoza, Spain). It consists on a C (Fortran) library, libTIDES, and a Mathematica package, MathTIDES. (MathTIDES requires Mathematica version >= 7.0) . Basic references: * A. Abad, R. Barrio, F. Blesa, M. Rodriguez, 2012. Algorithm 924: TIDES, a Taylor series Integrator for Differential EquationS, ACM TOMS. 39, no. 1, art. 5.... -
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ANGEL-Automatic differentiation Nested Graph Elimination Library is a template library using the Boost Graph Library and the Standard C++ Library; it provides sparse representations of c-graphs their dual line graphs and vertex, edge and face elimina
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GPDC
Gravity Potential Derivatives Calculator
GPDC is a C file to compute partial derivatives of a gravity potential up to any order by means of an automatic differentiation method.
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