update to v2.3.0

README.md

@@ -33,7 +33,7 @@ English | [简体中文](README_CN.md)
   </a>
   <!-- PyPI -->
   <a href="https://pypi.org/project/paddle-quantum/">
-    <img src="https://img.shields.io/badge/pypi-v2.2.2-orange.svg?style=flat-square&logo=pypi"/>
+    <img src="https://img.shields.io/badge/pypi-v2.3.0-orange.svg?style=flat-square&logo=pypi"/>
   </a>
   <!-- Python -->
   <a href="https://www.python.org/">
@@ -90,22 +90,19 @@ pip install -e .
 
 ### Environment setup for Quantum Chemistry module
 
-Our `qchem` module is based on `Psi4`, so before executing quantum chemistry, we have to install this Python package.
+Currently, our `qchem` module uses `PySCF` as its backend to compute molecular integrals, so before executing quantum chemistry, we have to install this Python package.
 
-> It is recommended that `Psi4` is installed in a Python 3.8 environment.
+> It is recommended that `PySCF` is installed in a Python environment whose Python version >=3.6.
 
-We highly recommend you to install `Psi4` via conda. **MacOS/Linux** user can use the command:
+We highly recommend you to install `PySCF` via conda. **MacOS/Linux** user can use the command:
 
 ```bash
-conda install psi4 -c psi4
+conda install -c pyscf pyscf
 ```
 
-For **Windows** user, the command is:
+> NOTE: For **Windows** user, if your operating system is Windows10, you can install `PySCF` in Ubuntu subsystem provided by Windows 10's App Store. `PySCF` can't run directly in Windows, so we are working hard to develop more quantum chemistry backends. Our support for Windows will be improved in the coming release of Paddle Quantum.
 
-```bash
-conda install psi4 -c psi4 -c conda-forge
-```
-**Note:** Please refer to [Psi4](https://psicode.org/installs/v14/) for more download options.
+**Note:** Please refer to [PySCF](https://pyscf.org/install.html) for more download options.
 
 ### Run example
 

README_CN.md

@@ -34,7 +34,7 @@
   </a>
   <!-- PyPI -->
   <a href="https://pypi.org/project/paddle-quantum/">
-    <img src="https://img.shields.io/badge/pypi-v2.2.2-orange.svg?style=flat-square&logo=pypi"/>
+    <img src="https://img.shields.io/badge/pypi-v2.3.0-orange.svg?style=flat-square&logo=pypi"/>
   </a>
   <!-- Python -->
   <a href="https://www.python.org/">
@@ -91,23 +91,19 @@ pip install -e .
 
 ### 量子化学模块的环境设置
 
-我们的量子化学模块是基于 `Psi4` 进行开发的，所以在运行量子化学模块之前需要先行安装该 Python 包。
+当前我们的量子化学模块在后端使用 `PySCF` 来计算各类分子积分，所以在运行量子化学模块之前需要先行安装该 Python 包。
 
-> 推荐在 Python3.8 环境中安装。
+> 推荐在 Python>=3.6 环境中安装。
 
-在安装 `psi4` 时，我们建议您使用 conda。对于 **MacOS/Linux** 的用户，可以使用如下指令。
+在安装 `PySCF` 时，我们建议您使用 conda。对于 **MacOS/Linux** 的用户，可以使用如下指令。
 
 ```bash
-conda install psi4 -c psi4
+conda install -c pyscf pyscf
 ```
 
-对于 **Windows** 用户，请使用
+> 注：对于 **Windows** 用户，如果操作系统为 Windows10，可以在其应用商店提供的 Ubuntu 子系统中利用上述命令安装 `PySCF`。`PySCF` 并不支持直接在 Windows 下运行，我们正在努力开发更多的量子化学后端，在量桨的下一版本中将会有对 Windows 更好的支持。 
 
-```bash
-conda install psi4 -c psi4 -c conda-forge
-```
-
-**注意：** 更多的下载方法请参考 [Psi4](https://psicode.org/installs/v14/)。
+**注意：** 更多的下载方法请参考 [PySCF](https://pyscf.org/install.html)。
 
 ### 运行
 
@@ -217,7 +213,7 @@ Paddle Quantum 使用 setuptools 的 develop 模式进行安装，相关代码
 
 ## 交流与反馈
 
-- 我们非常欢迎您通过 [Github Issues](https://github.com/PaddlePaddle/Quantum/issues) 来提交问题、报告与建议。
+- 我们非常欢迎您通过 [GitHub Issues](https://github.com/PaddlePaddle/Quantum/issues) 来提交问题、报告与建议。
 
 - 技术交流QQ群：1076223166
 

applications/README.md

@@ -0,0 +1,73 @@
+# Quantum Application Model Library
+
+- [Features](#features)
+- [Installation](#installation)
+- [How to Use](#how-to-use)
+- [Application List](#application-list)
+
+**Q**uantum **A**pplication **M**odel Library (QAML) is a collection of out-of-box practical quantum algorithms, it is developed by [Institute for Quantum Computing at Baidu](https://quantum.baidu.com/), and aims to be a "supermarket" of quantum solutions for industry users. Currently, models in QAML have covered popular areas listed below:
+
+- Artificial Intelligence
+- Medicine and Pharmaceuticals
+- Material Simulation
+- Financial Technology
+- Manufacturing
+- Data Analysis
+
+QAML is implemented on Paddle Quantum, a quantum machine learning platform, which can be found at https://qml.baidu.com and https://github.com/PaddlePaddle/Quantum.
+
+## Features
+
+- Industrialization: 10 models closely follow the 6 major industrial directions, covering hot topics such as artificial intelligence, chemical materials, manufacturing, finance, etc.
+- End-to-end: Linking the whole process from application scenarios to quantum computing and solving the last mile of quantum applications.
+- Out-of-box: No special configuration is required, the model is called directly by the Paddle Quantum, eliminating the tedious installation process.
+
+## Installation
+
+QAML depends on the `paddle-quantum` package. Users can install it by pip.
+
+```shell
+pip install paddle-quantum
+```
+
+For those who are using old versions of Paddle Quantum, simply run `pip install --upgrade paddle-quantum` to install the latest package.
+
+QAML locates in Paddle Quantum's GitHub repository, you can download the zip file contains QAML source code by clicking [this link](https://github.com/PaddlePaddle/Quantum/archive/refs/heads/master.zip). After unzipping the package, you will find all the models in the `applications` folder in the extracted folder.
+
+You can also use git to get the QAML source code.
+
+```shell
+git clone https://github.com/PaddlePaddle/Quantum.git
+cd Quantum/applications
+```
+
+You can check your installation by going to the `handwritten_digits_classification` folder under `applications` and running
+
+```shell
+python vsql_classification.py --example.toml
+```
+
+The installation is successful once the program terminates without errors.
+
+## How to Use
+
+In each application model, we provide Python scripts that can be run directly and the corresponding configuration files. The user can modify the configuration file to implement the corresponding requirements.
+
+Take handwritten digit classification as an example, it can be used by executing `python vsql_classification.py --example.toml` in the `handwritten_digits_classification` folder. We provide tutorials for each application model, which allows users to quickly understand and use it.
+
+## Application List
+
+*Continue update*
+
+Below we list instructions for all applications available in QAML, newly developed applications will be continuously integrated into QAML.
+
+1. [Handwritten digits classification](./handwritten_digits_classification/introduction_en.ipynb)
+2. [Molecular ground state energy & dipole moment calculation](./lithium_ion_battery/introduction_en.ipynb)
+3. [Text classification](./text_classification/introduction_en.ipynb)
+4. [Protein folding](./protein_folding/introduction_en.ipynb)
+5. [Medical image classification](./medical_image_classification/introduction_en.ipynb)
+6. [Quality detection](./quality_detection/introduction_en.ipynb)
+7. [Option pricing](./option_pricing/introduction_en.ipynb)
+8. [Quantum portfolio optimization](./portfolio_optimization/introduction_en.ipynb)
+9. [Regression](./regression/introduction_en.ipynb)
+10. [Quantum linear equation solver](./linear_solver/introduction_en.ipynb)

applications/README_CN.md

@@ -0,0 +1,73 @@
+# 量子应用模型库
+
+- [特色](#特色)
+- [安装](#安装)
+- [如何使用](#如何使用)
+- [应用列表](#应用列表)
+
+量子应用模型库（**Q**uantum **A**pplication **M**odel **L**ibrary, QAML）是一个开箱即用的实用量子应用模型集合，它由[百度量子计算研究所](https://quantum.baidu.com/)研发，旨在成为企业用户的量子解决方案“超市”。目前，QAML 中的模型已经覆盖了以下领域：
+
+- 人工智能
+- 医学制药
+- 材料模拟
+- 金融科技
+- 汽车制造
+- 数据分析
+
+QAML 基于量桨这一量子机器学习平台实现，关于量桨的内容可以参考 https://qml.baidu.com 和 https://github.com/PaddlePaddle/Quantum 。
+
+## 特色
+
+- 产业化：10 大应用模型紧贴 6 大产业方向，涵盖人工智能、化工材料、汽车制造、金融套利等热点话题。
+- 端到端：打通应用场景到量子算法的全流程，解决量子应用的最后一公里问题。
+- 开箱即用：无需特殊配置，通过量桨直接完成模型调用，省去繁琐安装环节。
+
+## 安装
+
+QAML 依赖于量桨（ `paddle-quantum` ）软件包。用户可以通过 pip 来安装：
+
+```shell
+pip install paddle-quantum
+```
+
+对于那些使用旧版量桨的用户，只需运行 `pip install --upgrade paddle-quantum` 即可安装最新版量桨。
+
+QAML 的内容在 Paddle Quantum 的 GitHub 仓库中，用户可以通过点击[此链接](https://github.com/PaddlePaddle/Quantum/archive/refs/heads/master.zip)下载包含 QAML 源代码的压缩包。QAML 的所有模型都在解压后的文件夹中的 `applications` 文件夹里。
+
+用户也可以使用 git 来获取 QAML 的源码文件。
+
+```shell
+git clone https://github.com/PaddlePaddle/Quantum.git
+cd Quantum/applications
+```
+
+用户可以进入到 `applications` 下的 `handwritten_digits_classification` 文件夹中，然后运行以下代码来检查安装是否成功。
+
+```shell
+python vsql_classification.py --example.toml
+```
+
+如果上面的程序没有报错、成功运行的话，则说明安装成功了。
+
+## 如何使用
+
+在每个应用模型中，我们都提供了可以直接运行的Python脚本和相应的配置文件。用户可以修改配置文件来实现对应的要求。
+
+以手写数字识别为例，用户可以通过执行 `handwritten_digits_classification` 中的 `python vsql_classification.py --example.toml` 命令来快速使用。我们为每个应用模型提供了教程，方便用户快速理解和上手使用。
+
+## 应用列表
+
+*持续更新中*
+
+我们列出了目前 QAML 的所有应用案例的教程，新开发的应用案例也会持续添加进来。
+
+1. [手写数字识别](./handwritten_digits_classification/introduction_cn.ipynb)
+2. [分子基态能量 & 偶极矩计算](./lithium_ion_battery/introduction_cn.ipynb)
+3. [中文文本分类](./text_classification/introduction_cn.ipynb)
+4. [蛋白质折叠](./protein_folding/introduction_cn.ipynb)
+5. [医学影像判别](./medical_image_classification/introduction_cn.ipynb)
+6. [材料表面质量检测](./quality_detection/introduction_cn.ipynb)
+7. [量子期权定价](./option_pricing/introduction_cn.ipynb)
+8. [投资组合优化](./portfolio_optimization/introduction_cn.ipynb)
+9. [回归分析](./regression/introduction_cn.ipynb)
+10. [线性方程组求解](./linear_solver/introduction_cn.ipynb)

applications/handwritten_digits_classification/example.toml

@@ -0,0 +1,9 @@
+
+task = 'test'
+image_path = 'data_0.png'
+is_dir = false
+model_path = 'vsql.pdparams'
+num_qubits = 10
+num_shadow = 2
+depth = 1
+classes = [0, 1]