Quantum machine learning

Course guarantor:

Lecturer:

Tutor:

Supervisor:

Department of Applied Mathematics

Synopsis:

The aim of the course is to introduce students to quantum machine learning. Students will first learn theoretically and practically about the quantum representation of classical data. Next, they will explore kernel methods, the quantum SVM model, and the use of quantum variational methods in supervised learning scenarios. The course will also introduce quantum neural networks and quantum generative adversarial models in unsupervised learning scenarios. The primary focus of the course is quantum algorithms for classical data. The exercises will use the pandas and qiskit libraries for Python to work with data and models.

Requirements:

Syllabus of lectures:

1. ntroduction, quantum representation of classical data.

2. Quantum random access memory.

3. Quantum PCA.

4. Quantum K-means and hierarchical clustering.

5. Nearest neighbour method.

6. Quantum kernel methods.

7. Quantum SVM.

8. Deep quantum learning - an introduction.

9. Classical and quantum Boltzmann machines.

10. Quantum generative adversarial networks.

11. Quantum perceptron.

12. Quantum neural networks.

13. Machine learning for quantum data.

Syllabus of tutorials:

1) Introduction, Quantum Representation of Classical Data, Amlithude Encoding of Data

2) Quantum kernel methods

3) Quantum SVM

4) Quantum K-means and hierarchical clustering

5) Quantum generative adversarial models

6) Quantum neural networks

Study Objective:

The aim of the course is to introduce students to quantum machine learning. Students will first learn theoretically and practically about the quantum representation of classical data. Next, they will explore kernel methods, the quantum SVM model, and the use of quantum variational methods in supervised learning scenarios. The course will also introduce quantum neural networks and quantum generative adversarial models in unsupervised learning scenarios. The primary focus of the course is quantum algorithms for classical data. The exercises will use the pandas and qiskit libraries for Python to work with data and models.

Study materials:

1. Schuld, M., Petruccione, F.: Machine Learning with Quantum Computers

Springer 2021

ISBN 978-3-030-83097-7

2. Ganguly, S.: Quantum Machine Learning: An Applied Approach

Apress 2021

ISBN 978-1-4842-7097-4

3. Pastorello, D.: Concise Guide to Quantum Machine Learning

Springer 2023

ISBN 978-981-19-6896-9

Note:

Information about the course and teaching materials can be found at https://courses.fit.cvut.cz/QNI-QML

The course is presented in Czech.

Further information:

https://courses.fit.cvut.cz/QNI-QML

No time-table has been prepared for this course

The course is a part of the following study plans:

• Quantum Informatics (compulsory elective course)