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
Information for Autumn 2024 Start 28 Oct 2024 programme students
- Course location
KTH Campus
- Duration
- 28 Oct 2024 - 13 Jan 2025
- Periods
- P2 (5.0 hp)
- Pace of study
25%
- Application code
51056
- Form of study
Normal Daytime
- Language of instruction
English
- Course memo
- Course memo is not published
- Number of places
Places are not limited
- Target group
- No information inserted
- Planned modular schedule
- [object Object]
- Schedule
- Schedule is not published
- Part of programme
- No information inserted
Contact
Stefano Markidis (markidis@kth.se)
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.
Equipment
A laptop or desktop computer is required to complete the final project.
Literature
The recommended textbook is "Supervised Learning with Quantum Computers" by M. Schuld and F. Petruccione. The book is available online in KTH's electronic library.
Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
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.
To pass the course, the student must pass an advanced final project (report and presentation).
Opportunity to complete the requirements via supplementary examination
No information inserted
Opportunity to raise an approved grade via renewed examination
No information inserted
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
Main field of study
This course does not belong to any Main field of study.
Education cycle
Third cycle
Add-on studies
No information inserted
Contact
Stefano Markidis (markidis@kth.se)