A series of lectures and exercises on:
- Qubits, Quantum Gates and Circuits
- Quantum Machine Learning with Parametrized Quantum Circuits
FDD3268 Applied Quantum Machine Learning 5.0 credits
Information per course offering
Course offerings are missing for current or upcoming semesters.
Course syllabus as PDF
Please note: all information from the Course syllabus is available on this page in an accessible format.
Course syllabus FDD3268 (Autumn 2023–)Headings with content from the Course syllabus FDD3268 (Autumn 2023–) are denoted with an asterisk ( )
Content and learning outcomes
Course disposition
Course contents
The course is divided into two modules:
Module I - Introduction to Qubits, Quantum Gates and Circuits: Introduction to quantum computing, qubits, quantum gates, and quantum circuits.
Module II - Quantum Machine Learning with Parametrized Quantum Circuits: data encoding, training parametrized quantum circuits, variational classification, quantum feature maps, and kernels.
In addition, we discuss the sustainability aspects of quantum machine learning.
Intended learning outcomes
After passing the course, the student will be able to:
- Describe and discuss how to develop a machine-learning application using a quantum-parametrized circuit
- Design and implement a machine learning application using quantum machine learning software
- List the differences between classical and quantum approaches for machine learning
- Compare the cost of quantum machine learning with traditional computing regarding power consumption
Literature and preparations
Specific prerequisites
Knowledge of basic machine learning techniques and linear algebra is required. Experience with Python is required.
Recommended prerequisites
Knowledge of basic machine learning techniques and linear algebra is required. Experience with Python is required.
Literature
You can find information about course literature either in the course memo for the course offering or in the course room in Canvas.
Examination and completion
Grading scale
P, F
Examination
- EXA1 - Examination, 5.0 credits, grading scale: P, F
Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.
The examiner may apply another examination format when re-examining individual students.
If the course is discontinued, students may request to be examined during the following two academic years.
To pass the course, the student must pass an advanced final project (report and presentation).
Examiner
Ethical approach
- All members of a group are responsible for the group's work.
- In any assessment, every student shall honestly disclose any help received and sources used.
- In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.
Further information
Course room in Canvas
Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.
Offered by
Education cycle
Third cycle