Machine learning has established itself as anirreplaceable tool in modern day decision making, and the rise of quantum computing is likely to push the capability of machine learning to new heights.
This course will take you through key concepts in quantum machine learning, such as parameterized quantum circuits, training circuits, and applying them to basic problems. It is based on an interactive textbook and finishes with a project that you can use to showcase what you've learnt.
Why do we recommend this course?
It is a condensed introduction to quantum machine learning for autonomous self-learners.
Which topics will be covered?
- Parameterized quantum circuits;
- Data encoding;
- Variational classification;
- Quantum feature maps and kernels;
- Quantum generative adversarial networks
What will I achieve?
By the end of the course, you‘ll be able to...
- understand the main concepts in this field;
- design supervised and unsupervised learning models;
- implement your own quantum machine learning project.
Which prerequisites do I need to fulfill?
- Undergraduate mathematics like linear algebra and calculus;
- Basics in Python programming;
- Fundamentals in quantum computing
We thank IBM Quantum and the Qiskit Community Team for producing and curating this course.
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