-[Use OpenFermion to read .xyz molecule configuration file](#use-openfermion-to-read-xyz-molecule-configuration-file)
-[Run programs](#run-programs)
-[Introduction and developments](#introduction-and-developments)
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...
@@ -44,24 +43,23 @@ Paddle Quantum aims at establishing a bridge between artificial intelligence (AI
### Install PaddlePaddle
Please refer to [PaddlePaddle](https://www.paddlepaddle.org.cn/install/quick)'s official installation and configuration page. This project requires PaddlePaddle 1.8.5.
This dependency will be automatically satisfied when users install Paddle Quantum. Please refer to [PaddlePaddle](https://www.paddlepaddle.org.cn/install/quick)'s official installation and configuration page. This project requires PaddlePaddle 1.8.5.
### Download and install Paddle Quantum
### Install Paddle Quantum
We recommend the following way of installing Paddle Quantum with `pip`,
```bash
git clone http://github.com/PaddlePaddle/quantum
pip install paddle-quantum
```
or download all the files and finish the installation locally,
```bash
git clone http://github.com/PaddlePaddle/quantum
cd quantum
pip install-e .
```
### Or use requirements to install dependencies
```bash
python -m pip install--upgrade-r requirements.txt
```
### Use OpenFermion to read .xyz molecule configuration file
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...
@@ -89,7 +87,7 @@ python main.py
### Quick start
[Paddle Quantum Quick Start Manual]((https://github.com/PaddlePaddle/Quantum/tree/master/introduction)) is probably the best place to get started with Paddle Quantum. Currently, we support online reading and running the Jupyter Notebook locally. The manual includes the following contents:
[Paddle Quantum Quick Start Manual](https://github.com/PaddlePaddle/Quantum/tree/master/introduction) is probably the best place to get started with Paddle Quantum. Currently, we support online reading and running the Jupyter Notebook locally. The manual includes the following contents:
- Detailed installation tutorials for Paddle Quantum
- Introduction to the basics of quantum computing and QNN
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...
@@ -112,11 +110,11 @@ We provide tutorials covering combinatorial optimization, quantum chemistry, qua
9.[Gibbs State Preparation](https://github.com/PaddlePaddle/Quantum/blob/master/tutorial/Gibbs)
10.[Variational Quantum Singular Value Decomposition (VQSVD)](https://github.com/PaddlePaddle/Quantum/blob/master/tutorial/VQSVD)
In addition, Paddle Quantum supports QNN training on GPU. For users who want to get into more details, please check out the tutorial [Use Paddle Quantum on GPU](https://github.com/PaddlePaddle/Quantum/tree/master/tutorial/GPU).
In addition, Paddle Quantum supports QNN training on GPU. For users who want to get into more details, please check out the tutorial [Use Paddle Quantum on GPU](https://github.com/PaddlePaddle/Quantum/blob/master/introduction/PaddleQuantum_GPU_EN.ipynb).
### API documentation
For those who are looking for explanation on the python class and functions provided in Paddle Quantum, we refer to our API documentation page.
For those who are looking for explanation on the python class and functions provided in Paddle Quantum, we refer to our [API documentation page](https://qml.baidu.com/api/introduction.html).
> We, in particular, denote that the current docstring specified in source code **is written in simplified Chinese**, this will be updated in later versions.
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...
@@ -131,7 +129,7 @@ We also highly encourage developers to use Paddle Quantum as a research tool to
"In this section, we will learn how to use the PaddlePaddle dynamic computational graph to find the minimum value of a multivariable function, for example,\n",
"[1] Mitarai, K., Negoro, M., Kitagawa, M. & Fujii, K. Quantum circuit learning. [Phys. Rev. A 98, 032309 (2018).](https://arxiv.org/abs/1803.00745)\n",
"\n",
"[2] Schuld, M., Bocharov, A., Svore, K. M. & Wiebe, N. Circuit-centric quantum classifiers. [Phys. Rev. A 101, 032308 (2020).](https://arxiv.org/abs/1804.00633)"
"[1] Mitarai, Kosuke, et al. Quantum circuit learning. [Physical Review A 98.3 (2018): 032309.](https://arxiv.org/abs/1803.00745)\n",
"\n",
"[2] Farhi, Edward, and Hartmut Neven. Classification with quantum neural networks on near term processors. [arXiv preprint arXiv:1802.06002 (2018).](https://arxiv.org/abs/1802.06002)\n",
"\n",
"[3] [Schuld, Maria, et al. Circuit-centric quantum classifiers. [Physical Review A 101.3 (2020): 032308.](https://arxiv.org/abs/1804.00633)\n"
"[1] Cao, Yudong, et al. Quantum chemistry in the age of quantum computing. [Chemical reviews 119.19 (2019): 10856-10915.](https://pubs.acs.org/doi/10.1021/acs.chemrev.8b00803)\n",
"[1] Cao, Yudong, et al. Quantum Chemistry in the Age of Quantum Computing. [Chemical reviews 119.19 (2019): 10856-10915.](https://pubs.acs.org/doi/10.1021/acs.chemrev.8b00803)\n",
"\n",
"[2] McArdle, Sam, et al. Quantum computational chemistry. [Reviews of Modern Physics 92.1 (2020): 015003.](https://journals.aps.org/rmp/abstract/10.1103/RevModPhys.92.015003)\n",
