slight update

166e0cbd · Quleaf · a08df5ab · 166e0cbd · 166e0cbd · 166e0cbd
7 changed file
--- a/README.md
+++ b/README.md
@@ -5,8 +5,7 @@ English | [简体中文](README_CN.md)
 - [Features](#features)
 - [Install](#install)
   - [Install PaddlePaddle](#install-paddlepaddle)
-   - [Download and install Paddle Quantum](#download-and-install-paddle-quantum)
+   - [Install Paddle Quantum](#install-paddle-quantum)
-   - [Or use requirements.txt to install dependencies](#or-use-requirements-to-install-dependencies)
   - [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)
@@ -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
@@ -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
@@ -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.
@@ -131,7 +129,7 @@ We also highly encourage developers to use Paddle Quantum as a research tool to
 > @misc{Paddlequantum,
 > title = {{Paddle Quantum}},
 > year = {2020},
-> url = {https://github.com/paddlepaddle/Quantum}, }
+> url = {https://github.com/PaddlePaddle/Quantum}, }
 So far, we have done several projects with the help of Paddle Quantum as a powerful QML development platform.
@@ -169,9 +167,9 @@ Paddle Quantum uses [Apache-2.0 license](LICENSE).
 ## References
-[1] [Quantum Computing - Wikipedia](https://en.wikipedia.org/wiki/Quantum_computing.)
+[1] [Quantum Computing - Wikipedia](https://en.wikipedia.org/wiki/Quantum_computing)
-[2] Nielsen, M. A. & Chuang, I. L. Quantum computation and quantum information. (Cambridge university press, 2010).
+[2] Nielsen, M. A. & Chuang, I. L. Quantum computation and quantum information. (2010).
 [3] Phillip Kaye, Laflamme, R. & Mosca, M. An Introduction to Quantum Computing. (2007).

--- a/README_CN.md
+++ b/README_CN.md
@@ -5,8 +5,7 @@
 - [特色](#特色)
 - [安装步骤](#安装步骤)
   - [安装 PaddlePaddle](#安装-paddlepaddle)
-   - [下载 Paddle Quantum 并安装](#下载-paddle-quantum-并安装)
+   - [安装 Paddle Quantum](#安装-paddle-quantum)
-   - [或使用 requirements.txt 安装依赖包](#或使用-requirementstxt-安装依赖包)
   - [使用 openfermion 读取 xyz 描述文件](#使用-openfermion-读取-xyz-描述文件)
   - [运行](#运行)
 - [入门与开发](#入门与开发)
@@ -44,24 +43,23 @@ Paddle Quantum（量桨）是基于百度飞桨开发的量子机器学习工具
 ### 安装 PaddlePaddle
-请参考 [PaddlePaddle](https://www.paddlepaddle.org.cn/install/quick) 安装配置页面。此项目需求 PaddlePaddle 1.8.5。
+当用户安装 Paddle Quantum 时会自动下载安装这个关键依赖包。关于 PaddlePaddle 更全面的安装信息请参考 [PaddlePaddle](https://www.paddlepaddle.org.cn/install/quick) 安装配置页面。此项目需求 PaddlePaddle 1.8.5。
-### 下载 Paddle Quantum 并安装
+### 安装 Paddle Quantum 
+我们推荐通过 `pip` 完成安装,
 ```bash
-git clone http://github.com/PaddlePaddle/quantum
+pip install paddle-quantum
 ```
+用户也可以选择下载全部文件后进行本地安装，
 ```bash
+git clone http://github.com/PaddlePaddle/quantum
 cd quantum
 pip install -e .
 ```
-### 或使用 requirements.txt 安装依赖包
-```bash
-python -m pip install --upgrade -r requirements.txt
-```
 ### 使用 openfermion 读取 xyz 描述文件
@@ -118,11 +116,11 @@ Paddle Quantum（量桨）建立起了人工智能与量子计算的桥梁，为
 - [吉布斯态的制备 (Gibbs State Preparation)](./tutorial/Gibbs)
 - [变分量子奇异值分解 (VQSVD)](./tutorial/VQSVD)
-此外，Paddle Quantum 也支持在 GPU 上进行量子机器学习的训练，具体的方法请参考案例：[在 GPU 上使用 Paddle Quantum](./tutorial/GPU)。
+此外，Paddle Quantum 也支持在 GPU 上进行量子机器学习的训练，具体的方法请参考案例：[在 GPU 上使用 Paddle Quantum](./introduction/PaddleQuantum_GPU_CN.ipynb)。
 ### API 文档
-我们为 Paddle Quantum 提供了独立的 [API 文档页面](https://paddle-quantum.readthedocs.io/zh_CN/latest/)，包含了供用户使用的所有函数和类的详细说明与用法。
+我们为 Paddle Quantum 提供了独立的 [API 文档页面](https://qml.baidu.com/api/introduction.html)，包含了供用户使用的所有函数和类的详细说明与用法。
 ### 开发
@@ -177,7 +175,7 @@ Paddle Quantum 使用 [Apache-2.0 license](LICENSE) 许可证。
 [1] [量子计算 - 百度百科](https://baike.baidu.com/item/%E9%87%8F%E5%AD%90%E8%AE%A1%E7%AE%97/11035661)
-[2] Nielsen, M. A. & Chuang, I. L. Quantum computation and quantum information. (Cambridge university press, 2010).
+[2] Nielsen, M. A. & Chuang, I. L. Quantum computation and quantum information. (2010).
 [3] Phillip Kaye, Laflamme, R. & Mosca, M. An Introduction to Quantum Computing. (2007).

--- a/introduction/PaddleQuantum_Tutorial_CN.ipynb
+++ b/introduction/PaddleQuantum_Tutorial_CN.ipynb
@@ -1174,7 +1174,7 @@
    "在这一节，我们学习如何用飞桨动态图机制找到一个多元函数的极小值\n",
    "\n",
    "$$\n",
-    "\\min_{\\boldsymbol{\\theta}}\\mathcal{L}(\\theta_1, \\theta_2, \\theta_3)\n",
+    "\\mathcal{L}(\\theta_1, \\theta_2, \\theta_3)\n",
    "= (\\theta_1)^2 + (\\theta_2)^2 + (\\theta_3)^2 + 10 \\tag{25}\n",
    "$$\n",
    "\n",
@@ -1293,7 +1293,7 @@
    "然后，我们定义损失函数为：\n",
    "\n",
    "$$\n",
-    "\\min_{\\boldsymbol{\\theta}}\\mathcal{L}(\\theta_1, \\theta_2, \\theta_3) \n",
+    "\\mathcal{L}(\\theta_1, \\theta_2, \\theta_3) \n",
    "= \\langle{\\phi} \\lvert H \\lvert {\\phi}\\rangle \n",
    "=  \\langle{0} \\lvert  U^{\\dagger}H U \\lvert {0}\\rangle. \\tag{29}\n",
    "$$\n",
@@ -1737,7 +1737,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.6.10"
+   "version": "3.7.8"
  },
  "toc": {
   "base_numbering": 1,

--- a/introduction/PaddleQuantum_Tutorial_EN.ipynb
+++ b/introduction/PaddleQuantum_Tutorial_EN.ipynb
@@ -965,7 +965,7 @@
    "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",
    "\n",
    "$$\n",
-    "\\min_{\\boldsymbol{\\theta}}\\mathcal{L}(\\theta_1, \\theta_2, \\theta_3)\n",
+    "\\mathcal{L}(\\theta_1, \\theta_2, \\theta_3)\n",
    "= (\\theta_1)^2 + (\\theta_2)^2 + (\\theta_3)^2 + 10.\n",
    "\\tag{25}\n",
    "$$\n",
@@ -1095,7 +1095,7 @@
    "Then, we define the loss function as\n",
    "\n",
    "$$\n",
-    "\\min_{\\boldsymbol{\\theta}}\\mathcal{L}(\\theta_1, \\theta_2, \\theta_3)\n",
+    "\\mathcal{L}(\\theta_1, \\theta_2, \\theta_3)\n",
    "= \\langle\\phi| H |\\phi\\rangle\n",
    "= \\langle0| U^{\\dagger}H U |0\\rangle.\n",
    "\\tag{29}\n",
@@ -1503,7 +1503,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.6.10"
+   "version": "3.7.8"
  },
  "toc": {
   "base_numbering": 1,

--- a/tutorial/Q-Classifier/QClassifier_CN.ipynb
+++ b/tutorial/Q-Classifier/QClassifier_CN.ipynb
@@ -4577,7 +4577,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.6.10"
+   "version": "3.7.8"
  },
  "toc": {
   "base_numbering": 1,

--- a/tutorial/Q-Classifier/QClassifier_EN.ipynb
+++ b/tutorial/Q-Classifier/QClassifier_EN.ipynb
@@ -4536,9 +4536,12 @@
    "\n",
    "## References\n",
    "\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"
   ]
  }
 ],
@@ -4558,7 +4561,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.6.10"
+   "version": "3.7.8"
  },
  "toc": {
   "base_numbering": 1,

--- a/tutorial/VQE/VQE_CN.ipynb
+++ b/tutorial/VQE/VQE_CN.ipynb
@@ -413,7 +413,7 @@
   "source": [
    "### 配置训练模型 - 模型参数\n",
    "\n",
-    "在进行量子神经网络的训练之前，我们还需要进行一些训练的超参数设置，主要是学习速率 (LR, learning rate)、迭代次数(ITR, iteration)和量子神经网络计算模块的深度 (D, Depth)。这里我们设定学习速率为0.5, 迭代次数为50次。读者不妨自行调整来直观感受下超参数调整对训练效果的影响。"
+    "在进行量子神经网络的训练之前，我们还需要进行一些训练的超参数设置，主要是学习速率（LR, learning rate）、迭代次数（ITR, iteration）和量子神经网络计算模块的深度（D, Depth）。这里我们设定学习速率为 0.5, 迭代次数为 50 次。读者不妨自行调整来直观感受下超参数调整对训练效果的影响。"
   ]
  },
  {
@@ -1903,7 +1903,7 @@
    "\n",
    "## 参考文献\n",
    "\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",
    "\n",
@@ -1938,7 +1938,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.8.3"
+   "version": "3.7.8"
  },
  "toc": {
   "base_numbering": 1,