Getting Started with Quantum Machine Learning

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.