semvec/pypi-packages huggingface dataset:
luna-quantum 1.1.3
Python SDK for Aqarios' Luna Platform
Requires Python: <3.15,>=3.11.0
Keywords: aqarios, luna, quantum computing, quantum optimization, optimization, sdk

# Luna Quantum SDK
Luna is a platform developed by [Aqarios](https://aqarios.com/) designed to facilitate access to and utilization of quantum computing for both industry and academia. The SDK serves as a Python wrapper for interfacing with the API, offering a more intuitive means of engaging with the platform. This library grants access to the LunaSolve and LunaQ services.

# Installation
The package is released on PyPI and can be installed via pip:
```
pip install --upgrade luna-quantum
```

# Getting Started
For more details and tutorials on how to use the Luna SDK, refer to the documentation located here: [Aqarios Luna](https://docs.aqarios.com/).
libmim-solvers 0.2.0
A fast and flexible implementation of Rigid Body Dynamics algorithms and their analytical derivatives
Requires Python: >=3.8

# mim_solvers
Implementation of efficient numerical optimal control solvers.
In particular, the Sequential Quadratic Programming (SQP) solver described in [this paper](https://laas.hal.science/hal-04330251) solves nonlinear constrained OCPs efficiently by leveraging sparsity.

All the solvers are implemented based on the API of [Crocoddyl](https://github.com/loco-3d/crocoddyl/tree/devel) (v3).
In other words, our solvers take as input a `crocoddyl.ShootingProblem`.

Examples on how to use the solvers can be found in the `examples` directory.

# Dependencies
- [Pinocchio](https://github.com/stack-of-tasks/pinocchio) (rigid-body dynamics computations)
- [Crocoddyl](https://github.com/loco-3d/crocoddyl) (optimal control library)
- [ProxSuite](https://github.com/Simple-Robotics/proxsuite) (quadratic programming) (optional)

# Installation

  ## Using conda

`conda install mim-solvers --channel conda-forge`


  ## Using CMake
`git clone --recursive https://github.com/machines-in-motion/mim_solvers.git`

`cd mim_solvers && mkdir build && cd build`

`cmake .. [-DCMAKE_BUILD_TYPE=Release] [-DCMAKE_INSTALL_PREFIX=...]`

`make [-j6] && make install`

You can also run unittests using `ctest` and benchmarks using `./benchmarks/ur5` or `./benchmarks/solo12` from the build directory.


# Contributors

-   [Armand Jordana](https://github.com/ajordana) (NYU): main developer and manager of the project
-   [Sébastien Kleff](https://github.com/skleff1994) (NYU): main developer and manager of the project
-   [Avadesh Meduri](https://github.com/avadesh02) (NYU): main developer and manager of the project
-   [Ludovic Righetti](https://engineering.nyu.edu/faculty/ludovic-righetti) (NYU): project instructor
-   [Justin Carpentier](https://jcarpent.github.io) (INRIA): project instructor
-   [Nicolas Mansard](http://projects.laas.fr/gepetto/index.php/Members/NicolasMansard) (LAAS-CNRS): project instructor
-   [Yann de Mont-Marin](https://github.com/ymontmarin) (INRIA): Conda integration and support
-   [Louis Montaut](https://github.com/lmontaut) (INRIA): CMake support
-   [Guilhem Saurel](https://github.com/nim65s) (LAAS-CNRS): CMake & pip packaging support
-   [Jianghan ZHang](https://github.com/JianghanZHang) (NYU): features extension