Paddle Quantum (量桨) is a quantum machine learning (QML) toolkit developed based on Baidu PaddlePaddle. It provides a platform to construct and train quantum neural networks (QNNs) with easy-to-use QML development kits suporting combinatorial optimization, quantum chemistry and other cutting-edge quantum applications, making PaddlePaddle the first and only deep learning framework in China that supports quantum machine learning.
Paddle Quantum aims at establishing a bridge between artificial intelligence (AI) and quantum computing (QC). It has been utilized for developing several quantum machine learning applications. With the PaddlePaddle deep learning platform empowering QC, Paddle Quantum provides strong support for scientific research community and developers in the field to easily develop QML applications. Moreover, it provides a learning platform for quantum computing enthusiasts.
## 特色
## Features
- 易用性
- Easy-to-use
- 高效搭建量子神经网络
- Build quantum neural networks efficiently
- 多种量子神经网络模板
- Various quantum neural network templates
- 丰富的量子算法教程（10+用例）
- 10+ QML algorithm tutorials
- 可拓展性
- Scalability
- 支持通用量子电路模型
- Support universal quantum circuit model
- 高性能模拟器支持20多个量子比特的模拟运算
- Provide multiple optimization tools and GPU mode
- 提供多种优化工具和 GPU 加速
- High-performance simulator that supports more than 20 qubits
- 特色工具集
- Featured Toolkits
- 提供组合优化和量子化学等前沿领域的计算工具箱
- Provides computational toolboxes in cutting-edge fields such as combinatorial optimization and quantum chemistry
Please refer to [PaddlePaddle](https://www.paddlepaddle.org.cn/install/quick)'s official installation and configuration page. This project requires PaddlePaddle 1.8.5.
### 下载 Paddle Quantum 并安装
### Download and install Paddle Quantum
```bash
```bash
git clone http://github.com/PaddlePaddle/quantum
git clone http://github.com/PaddlePaddle/quantum
...
@@ -55,120 +57,119 @@ cd quantum
...
@@ -55,120 +57,119 @@ cd quantum
pip install-e .
pip install-e .
```
```
### 或使用 requirements.txt 安装依赖包
### Or use requirements to install dependencies
```bash
```bash
python -m pip install--upgrade-r requirements.txt
python -m pip install--upgrade-r requirements.txt
```
```
### 使用 openfermion 读取 xyz 描述文件
### Use OpenFermion to read xyz molecule configuration file
> 仅在 macOS 和 linux 下可以使用 openfermion 读取 xyz 描述文件。
> Only macOS and linux users can use OpenFermion to read .xyz description files.
Once the user confirms the above OS constraint, OpenFermion can be installed with the following command. These packages are used for quantum chemistry calculations and could be potentially used in the VQE tutorial.
Now, you can try to run a program to verify whether Paddle Quantum has been installed successfully. Here we take quantum approximate optimization algorithm (QAOA) as an example.
```bash
```bash
cd paddle_quantum/QAOA/example
cd paddle_quantum/QAOA/example
python main.py
python main.py
```
```
> 关于 QAOA 的介绍可以参考我们的 [QAOA 教程](./tutorial/QAOA)。
> For the introduction of QAOA, please refer to our [QAOA tutorial](https://github.com/PaddlePaddle/Quantum/tree/master/tutorial/QAOA).
这里，我们提供了一份[**入门手册**](./introduction)方便用户快速上手 Paddle Quantum。目前支持 PDF 阅读和运行 Jupyter Notebook 两种方式。内容上，该手册包括以下几个方面：
### Quick start
- Paddle Quantum 的详细安装教程
[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:
- 量子计算的基础知识介绍
- Paddle Quantum 的使用介绍
- PaddlePaddle 飞桨优化器使用教程
- 具体的量子机器学习案例—VQE
### 案例入门
- Detailed installation tutorials for Paddle Quantum
- Introduction to the basics of quantum computing and QNN
- Introduction on the operation modes of Paddle Quantum
- A quick tutorial on PaddlePaddle's dynamic computational graph and optimizers
- A case study on Quantum Machine Learning -- Variational Quantum Eigensolver (VQE)
在这里，我们提供了涵盖量子优化、量子化学、量子机器学习等多个领域的案例供大家学习。与[入门手册](./introduction)类似，每个教程目前支持 PDF 阅读和运行 Jupyter Notebook 两种方式。我们推荐用户下载 Notebook 后，本地运行进行实践。
We provide tutorials covering combinatorial optimization, quantum chemistry, quantum classification and other popular QML research topics. Each tutorial currently supports reading on our website and running Jupyter Notebooks locally. For interested developers, we recommend them to download Jupyter Notebooks and play around with it. Here is the tutorial list,
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).
For those who are looking for explanation on the python class and functions provided in Paddle Quantum, we refer to our API documentation page.
### 开发
> We, in particular, denote that the current docstring specified in source code **is written in simplified Chinese**, this will be updated in later versions.
Users are encouraged to contact us through [Github Issues](https://github.com/PaddlePaddle/Quantum/issues) or email quantum@baidu.com with general questions, unfixed bugs, and potential improvements. We hope to make Paddle Quantum better together with the community!
We also highly encourage developers to use Paddle Quantum as a research tool to develop novel QML algorithms. If your work uses Paddle Quantum, feel free to send us a notice via quantum@baidu.com. We are always excited to hear that! Cite us with the following BibTeX:
So far, we have done several projects with the help of Paddle Quantum as a powerful QML development platform.
[1] Wang, Y., Li, G. & Wang, X. Variational quantum Gibbs state preparation with a truncated Taylor series. arXiv:2005.08797 (2020). [[pdf](https://arxiv.org/pdf/2005.08797.pdf)]
[1] Wang, Y., Li, G. & Wang, X. Variational quantum Gibbs state preparation with a truncated Taylor series. arXiv:2005.08797 (2020). [[pdf](https://arxiv.org/pdf/2005.08797.pdf)]
[2] Wang, X., Song, Z. & Wang, Y. Variational Quantum Singular Value Decomposition. arXiv:2006.02336 (2020). [[pdf](https://arxiv.org/pdf/2006.02336.pdf)]
[2] Wang, X., Song, Z. & Wang, Y. Variational Quantum Singular Value Decomposition. arXiv:2006.02336 (2020). [[pdf](https://arxiv.org/pdf/2006.02336.pdf)]
## FAQ
[3] Li, G., Song, Z. & Wang, X. VSQL: Variational Shadow Quantum Learning for Classification. arXiv:2012.08288 (2020). [[pdf]](https://arxiv.org/pdf/2012.08288.pdf), to appear at **AAAI 2021** conference.
1. 问：**研究量子机器学习有什么意义？它有哪些应用场景？**
[4] Chen, R., Song, Z., Zhao, X. & Wang, X. Variational Quantum Algorithms for Trace Distance and Fidelity Estimation. arXiv:2012.05768 (2020). [[pdf]](https://arxiv.org/pdf/2012.05768.pdf)
[5] Wang, K., Song, Z., Zhao, X., Wang Z. & Wang, X. Detecting and quantifying entanglement on near-term quantum devices. arXiv:2012.14311 (2020). [[pdf]](https://arxiv.org/pdf/2012.14311.pdf)
1.**Question:** What is quantum machine learning? What are the applications?
3. 问：**现阶段没有规模化的量子硬件，怎么开发量子应用？**
**Answer:** Quantum machine learning (QML) is an interdisciplinary subject that combines quantum computing (QC) and machine learning (ML). On the one hand, QML utilizes existing artificial intelligence technology to break through the bottleneck of quantum computing research. On the other hand, QML uses the information processing advantages of quantum computing to promote the development of traditional artificial intelligence. QML is not only suitable for quantum chemical simulations (with Variational Quantum Eigensolver) and other quantum problems. It also help researchers to solve classical optimization problems including knapsack problem, traveling salesman problem, and Max-Cut problem through the Quantum Approximate Optimization Algorithm.
2.**Question:** I want to study QML, but I don't know much about quantum computing. Where should I start?
4. 问：**量桨有哪些优势？**
**Answer:** *Quantum Computation and Quantum Information* by Nielsen & Chuang is the classic introductory textbook to QC. We recommend readers to study Chapter 1, 2, and 4 of this book first. These chapters introduce the basic concepts, provide solid mathematical and physical foundations, and discuss the quantum circuit model widely used in QC. Readers can also go through Paddle Quantum's quick start guide, which contains a brief introduction to QC and interactive examples. After building a general understanding of QC, readers can try some cutting-edge QML applications provided as tutorials in Paddle Quantum.
3.**Question:** Currently, there is no fault-tolerant large-scale quantum hardware. How can we develop quantum applications?
5. 问：**非常想试用量桨，该怎么入门呢？**
**Answer:** The development of useful algorithms does not necessarily require a perfect hardware. The latter is more of an engineering problem. With Paddle Quantum, one can develop, simulate, and verify the validity of self-innovated quantum algorithms. Then, researchers can choose to implement these tested quantum algorithms in a small scale hardware and see the actual performance of it. Following this line of reasoning, we can prepare ourselves with many candidates of useful quantum algorithms before the age of matured quantum hardware.
4.**Question:** What are the advantages of Paddle Quantum?
答：建议新用户首先阅读量桨的[入门手册](./introduction)，它包含量桨详细的安装步骤以及入门教程。另外，量桨提供了丰富的[量子机器学习案例](./tutorial)，以 Jupyter Notebook 和 PDF 的方式呈现，方便用户学习和实践。如在学习和使用过程中遇到任何问题，欢迎用户通过 [Github Issues](https://github.com/PaddlePaddle/Quantum/issues) 以及技术交流QQ群（1076223166）与我们交流。
**Answer:** Paddle Quantum is an open-source QML toolkit based on Baidu PaddlePaddle. As the first open-source and industrial level deep learning platform in China, PaddlePaddle has the leading ML technology and rich functionality. With the support of PaddlePaddle, especially its dynamic computational graph mechanism, Paddle Quantum could easily train a QNN and with GPU acceleration. In addition, based on the high-performance quantum simulator developed by Institute for Quantum Computing (IQC) at Baidu, Paddle Quantum can simulate more than 20 qubits on personal laptops. Finally, Paddle Quantum provides many open-source QML tutorials for readers from different backgrounds.
[5] [Schuld, M., Sinayskiy, I. & Petruccione, F. An introduction to quantum machine learning. Contemp. Phys. 56, 172–185 (2015).](https://www.tandfonline.com/doi/abs/10.1080/00107514.2014.964942)
[5] [Schuld, M., Sinayskiy, I. & Petruccione, F. An introduction to quantum machine learning. Contemp. Phys. 56, 172–185 (2015).](https://www.tandfonline.com/doi/abs/10.1080/00107514.2014.964942)
[6] [Benedetti, M., Lloyd, E., Sack, S. & Fiorentini, M. Parameterized quantum circuits as machine learning models. Quantum Sci. Technol. 4, 043001 (2019).](https://iopscience.iop.org/article/10.1088/2058-9565/ab4eb5)
[6] [Benedetti, M., Lloyd, E., Sack, S. & Fiorentini, M. Parameterized quantum circuits as machine learning models. Quantum Sci. Technol. 4, 043001 (2019).](https://iopscience.iop.org/article/10.1088/2058-9565/ab4eb5)
[1] Wang, Y., Li, G. & Wang, X. Variational quantum Gibbs state preparation with a truncated Taylor series. arXiv:2005.08797 (2020). [[pdf](https://arxiv.org/pdf/2005.08797.pdf)]
[2] Wang, X., Song, Z. & Wang, Y. Variational Quantum Singular Value Decomposition. arXiv:2006.02336 (2020). [[pdf](https://arxiv.org/pdf/2006.02336.pdf)]
[3] Li, G., Song, Z. & Wang, X. VSQL: Variational Shadow Quantum Learning for Classification. arXiv:2012.08288 (2020). [[pdf]](https://arxiv.org/pdf/2012.08288.pdf), to appear at **AAAI 2021** conference.
[4] Chen, R., et al. Variational Quantum Algorithms for Trace Distance and Fidelity Estimation. arXiv:2012.05768 (2020). [[pdf]](https://arxiv.org/pdf/2012.05768.pdf)
[5] Wang, K., et al. Detecting and quantifying entanglement on near-term quantum devices. arXiv:2012.14311 (2020). [[pdf]](https://arxiv.org/pdf/2012.14311.pdf)
[2] Nielsen, M. A. & Chuang, I. L. Quantum computation and quantum information. (Cambridge university press, 2010).
[3] Phillip Kaye, Laflamme, R. & Mosca, M. An Introduction to Quantum Computing. (2007).
[4] [Biamonte, J. et al. Quantum machine learning. Nature 549, 195–202 (2017).](https://www.nature.com/articles/nature23474)
[5] [Schuld, M., Sinayskiy, I. & Petruccione, F. An introduction to quantum machine learning. Contemp. Phys. 56, 172–185 (2015).](https://www.tandfonline.com/doi/abs/10.1080/00107514.2014.964942)
[6] [Benedetti, M., Lloyd, E., Sack, S. & Fiorentini, M. Parameterized quantum circuits as machine learning models. Quantum Sci. Technol. 4, 043001 (2019).](https://iopscience.iop.org/article/10.1088/2058-9565/ab4eb5)
