Getting Started with Quantum Machine Learning

Offered By
Coursera Project Network
In this Guided Project, you will:

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

Learn the workflow for developing with and build a custom Plugin

Convert a Tensorflow Keras network Quantum by layer.

Clock2 hours
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

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.

Skills you will develop

Matrix MultiplicationMolecular ModellingDifferentiable FunctionMatrices

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

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

  2. Learn how 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 API

  6. Building your own Pennylane Plugin

  7. Turning Quantum Nodes into Tensorflow Keras Layers

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

Frequently asked questions

Frequently Asked Questions

More questions? Visit the Learner Help Center.