About the course
In seven lectures and five hands-on labs, we explore how quantum computing can support machine learning. Using PennyLane, we will build circuits, encode data, study quantum clustering and classifiers, benchmark real hardware, and examine how noise and error mitigation affect results.
Assessment
The course includes six quizzes and two homework assignments. Grades are weighted as follows:
| Component | Weight |
|---|---|
| Six quizzes | 40% |
| Two homework assignments | 50% |
| Participation | 10% |
Assignment instructions, due dates, and quizzes will be posted in Canvas.
Course materials
Course resources and assignments will be shared here and on Canvas.
Books and additional reading
- Practical Quantum Machine Learning and Quantum Optimization
- Machine Learning with Quantum Computers
- A Gentle Introduction to Quantum Computing
Class schedule
Select a topic to view the session details. All dates are in 2026.
| Date | Session | Topic |
|---|---|---|
| Oct 20 | Lecture 1 | Quantum Computing Foundations, Part 1 |
| Oct 22 | Lab 1 | PennyLane Fundamentals |
| Oct 27 | Lecture 2 | Quantum Computing Foundations, Part 2 |
| Oct 29 | Lecture 3 | Data Encoding and Quantum k-means |
| Nov 3 | No class | Democracy Day |
| Nov 5 | Lecture 4 | Perceptrons, MLPs, and Quantum Neural Networks |
| Nov 10 | Lab 2 | Encoding and Quantum k-means |
| Nov 12 | Lab 3 | Real Hardware and Benchmarking |
| Nov 17 | Lecture 5 | Linear and Variational Quantum Classifiers |
| Nov 19 | Lab 4 | Variational Quantum Classifiers in PennyLane |
| Nov 24 | Lecture 6 | Quantum Hardware, Part 1: Qubit Modalities and the QPU |
| Nov 26 | No class | Thanksgiving |
| Dec 1 | Lab 5 | Noise, Error Mitigation, and the Limits of NISQ |
| Dec 3 | Lecture 7 | Quantum Hardware, Part 2: Noise, Error, and the NISQ Era |
Course updates: We will post assignments, deadlines, and any schedule changes in Canvas.