# PyQuafu
Python toolkit for submitting quantum circuits on the superconducting quantum computing cloud [Quafu](http://quafu.baqis.ac.cn/).
## Introduction
PyQuafu is developed for the users of [Quafu](http://quafu.baqis.ac.cn/) to construct, compile and execute quantum circuits on real quantum devices. One can use PyQuafu to interact with different quantum backends provides by the experimental group of [Quafu](http://quafu.baqis.ac.cn/).
## Installation
You can directly install via PyPI,
```
pip install pyquafu
```
or build from source
```
pip install -r requirements.txt
python setup.py install
```
## GPU support
To install PyQuafu with GPU-based circuit simulator, you need build from the source and make sure that [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) is installed. You can run
```
python setup.py install -DUSE_GPU=ON
```
to install the GPU version. If you further have [cuQuantum](https://developer.nvidia.com/cuquantum-sdk) installed, you can install PyQuafu with cuQuantum support.
```
python setup.py install -DUSE_GPU=ON -DUSE_CUQUANTUM=ON
```
## Document
Please see the website [docs](https://scq-cloud.github.io/).
## Note
If you are using an Apple silicon Mac and meet the error "illegal hardware instruction", please confirm whether you have updated to the arm64 version of Anaconda (see https://github.com/abess-team/abess/issues/310).
## Examples
### 1.quantum_rl
The example shows quantum reinforcement learning interacts with Quafu to solve CartPole environment.
Refer to https://github.com/enchanted123/quantum-RL-with-quafu for more details.
## Authors
This project is developed by the quantum cloud computing team at the Beijing Academy of Quantum Information Sciences.
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"description": "# PyQuafu\n\nPython toolkit for submitting quantum circuits on the superconducting quantum computing cloud [Quafu](http://quafu.baqis.ac.cn/). \n\n\n## Introduction\n\nPyQuafu is developed for the users of [Quafu](http://quafu.baqis.ac.cn/) to construct, compile and execute quantum circuits on real quantum devices. One can use PyQuafu to interact with different quantum backends provides by the experimental group of [Quafu](http://quafu.baqis.ac.cn/). \n\n## Installation\n\nYou can directly install via PyPI,\n\n```\npip install pyquafu \n```\n \nor build from source\n\n```\npip install -r requirements.txt\npython setup.py install\n```\n\n## GPU support\nTo install PyQuafu with GPU-based circuit simulator, you need build from the source and make sure that [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) is installed. You can run\n\n```\npython setup.py install -DUSE_GPU=ON\n```\nto install the GPU version. If you further have [cuQuantum](https://developer.nvidia.com/cuquantum-sdk) installed, you can install PyQuafu with cuQuantum support.\n```\npython setup.py install -DUSE_GPU=ON -DUSE_CUQUANTUM=ON\n```\n\n\n## Document\nPlease see the website [docs](https://scq-cloud.github.io/).\n\n## Note\nIf you are using an Apple silicon Mac and meet the error \"illegal hardware instruction\", please confirm whether you have updated to the arm64 version of Anaconda (see https://github.com/abess-team/abess/issues/310).\n\n## Examples\n\n### 1.quantum_rl\n\nThe example shows quantum reinforcement learning interacts with Quafu to solve CartPole environment. \n\nRefer to https://github.com/enchanted123/quantum-RL-with-quafu for more details.\n\n## Authors\nThis project is developed by the quantum cloud computing team at the Beijing Academy of Quantum Information Sciences.\n",
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