We have prepared a quick start guide and several tutorials to help users quickly learn how to use Paddle Quantum.
We provide a quick start guide to help users get started with Paddle Quantum. You can read it online on this website or download and run it with Jupyter Notebook. This guide covers the following contents:
- Introduction to quantum computing and quantum neural networks (QNNs)
- Introduction to Variational Quantum Algorithms (VQAs)
- Introduction to Paddle Quantum
- PaddlePaddle optimizer tutorial
- Introduction to the quantum chemistry module in Paddle Quantum
- How to train QNN with GPU
We provide use cases covering quantum simulation, machine learning, combinatorial optimization, local operations and classical communication (LOCC), and other popular quantum machine learning research topics. Similar to the quick start guide, you can read each tutorial online or download and run Jupyter Notebooks locally. For interested developers, we recommend them to download Jupyter Notebooks and play around with it. Here are the tutorial categories:
With the latest LOCCNet module, Paddle Quantum can efficiently simulate distributed quantum information processing tasks. Interested readers can start with this tutorial on LOCCNet. In addition, Paddle Quantum supports quantum neural network training on GPU. For users who want to get into more details, please check out the tutorial: Use Paddle Quantum on GPU. Moreover, Paddle Quantum could design robust quantum algorithms under noise. For more information, please see Noise tutorial。
In a recent update, the measurement-based quantum computation (MBQC) module has been added to Paddle Quantum. Unlike the conventional quantum circuit model, MBQC has its unique way of computing. Interested readers are welcomed to read our tutorials on how to usethe MBQC module and its use cases.