diff --git a/README.md b/README.md index 7670978864f2703e7a523db5c9af46baa6a68316..4d04e52a648a42666a52a1902e077f440143a9dc 100644 --- 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) - - [Or use requirements.txt to install dependencies](#or-use-requirements-to-install-dependencies) + - [Install Paddle Quantum](#install-paddle-quantum) - [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). diff --git a/README_CN.md b/README_CN.md index f1706ebc4606434c5d9f5ba0a1b7b7e2dfe2f0c1..e2d2d0c198336a92d9a1de7a8aea90a04805b71b 100644 --- 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) - - [或使用 requirements.txt 安装依赖包](#或使用-requirementstxt-安装依赖包) + - [安装 Paddle Quantum](#安装-paddle-quantum) - [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). diff --git a/introduction/PaddleQuantum_Tutorial_CN.ipynb b/introduction/PaddleQuantum_Tutorial_CN.ipynb index 6c4dc864694613a7b76fb8e275f79d3abe524954..8e3907096393612a679aca989803441895c3e416 100644 --- 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, diff --git a/introduction/PaddleQuantum_Tutorial_EN.ipynb b/introduction/PaddleQuantum_Tutorial_EN.ipynb index 028c48a2984a4743f4882ed6ae279b02e0d2ed18..2ce3121cac45f2026bd7d45f59c6f75de4b45b7e 100644 --- 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, diff --git a/tutorial/Q-Classifier/QClassifier_CN.ipynb b/tutorial/Q-Classifier/QClassifier_CN.ipynb index b712a8db2187250184b1c742867a13db1a969c54..e2a643d4ff3643148a29e75364fb68fe1ec05fc5 100644 --- 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, diff --git a/tutorial/Q-Classifier/QClassifier_EN.ipynb b/tutorial/Q-Classifier/QClassifier_EN.ipynb index 5650374bed19f08b66e740230386e0161a0c12ee..8c412af5b9896f76f407cb65d769929523bffac6 100644 --- 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, diff --git a/tutorial/VQE/VQE_CN.ipynb b/tutorial/VQE/VQE_CN.ipynb index 6d2ea45d779950a34ec4c39d7b9a838ad8b31d8a..d63390ff95092b4ccfb08ecaed6d0d214a6328e4 100644 --- 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,