Getting Started with Quantum Machine Learning

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Coursera Project Network
在此指導 項目中,您將:

Utilize Pennylane.ai as a cross-platform Python library for differentiable programming of quantum computers.

Learn the workflow for developing with Pennylane.ai and build a custom Pennylane.ai Plugin

Convert a Tensorflow Keras network Quantum by layer.

Clock2 hours
Advanced高級設置
Cloud無需下載
Video分屏視頻
Comment Dots英語(English)
Laptop僅限桌面

In this 2-hour long project-based course, you will learn basic principles of how machine learning can benefit from work, and how this can be implemented in Python using the Pennylane library by Xanadu. The Future is Quantum. You've heard the hype. Quantum Computing represents a completely new paradigm in the computing realm, posed to revolutionize entire industries and bring amazing new innovations as they are used for purposes such as material design, pharmaceutical design, genetic and molecular simulations, and weather simulations. The most exciting advancement just may be in the field of Artificial Intelligence and Machine Learning. Quantum computers can theoretically speed up matrix multiplications and process massive amounts of data very quickly, and thus may represent a paradigm shift in AI and ML. Most of this work is yet to be done. That's where you come in. In this project, you will learn how to utilize several software libraries to code quantum algorithms and encode data for use in both classical simulations of quantum devices or actual quantum devices that are available for use over the Internet through vendors such as IBM. I would encourage learners to experiment- How easy is it to add more layers without using frameworks like Tensorflow? What if we add more nodes? What limitations do we come across? The learner is highly encouraged to experiment beyond the scope of the course. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

您要培養的技能

Matrix MultiplicationMolecular ModellingDifferentiable FunctionMatrices

分步進行學習

在與您的工作區一起在分屏中播放的視頻中,您的授課教師將指導您完成每個步驟:

  1. Learn the Bare Basics of Quantum Computing and Quantum Machine Learning or QML.

  2. Learn how Pennylane.ai is used and what it does.

  3. Build Qnodes and Customized Templates

  4. Calculating Autograd and Loss Function with Quantum Computing using Pennylane

  5. Developing with the Pennylane.ai API

  6. Building your own Pennylane Plugin

  7. Turning Quantum Nodes into Tensorflow Keras Layers

指導項目工作原理

您的工作空間就是瀏覽器中的雲桌面,無需下載

在分屏視頻中,您的授課教師會為您提供分步指導

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