These notebooks are the source files for the Quantum machine learning course of the Qiskit Textbook.

This course contains around eight hours of content, and is aimed at self-learners who are comfortable with undergraduate-level mathematics and quantum computing fundamentals. This course will take you through key concepts in quantum machine learning, such as parameterized quantum circuits, training these circuits, and applying them to basic problems. By the end of the course, you'll understand the state of the field, and you'll be familiar with recent developments in both supervised and unsupervised learning such as quantum kernels and general adversarial networks. This course finishes with a project that you can use to showcase what you've learnt.

This course was created by IBM Quantum with the help of Qiskit Advocates through the Qiskit Advocate Mentoring Program.

1. Introduction
2. Parameterized quantum circuits
3. Data encoding
4. Training parameterized quantum circuits
5. Supervised learning
6. Variational classification
7. Quantum feature maps and kernels
8. Unsupervised learning
9. Quantum generative adversarial networks
10. Project