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DD2368 Quantum Neural Networks 7.5 credits

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Information per course offering

Termin

Information for Autumn 2025 Start 27 Oct 2025 programme students

Course location

KTH Campus

Duration
27 Oct 2025 - 12 Jan 2026
Periods

Autumn 2025: P2 (7.5 hp)

Pace of study

50%

Application code

50263

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
Open to all master's programmes as long as the course can be included in the programme
Planned modular schedule
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Contact

Examiner
No information inserted
Course coordinator
No information inserted
Teachers
No information inserted

Course syllabus as PDF

Please note: all information from the Course syllabus is available on this page in an accessible format.

Course syllabus DD2368 (Autumn 2025–)
Headings with content from the Course syllabus DD2368 (Autumn 2025–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

Quantum computing principles and their application in machine learning.

Quantum bits, quantum gates and quantum circuits.

Many-quantum bit systems and quantum entanglement.

Differentiable quantum programming techniques, variational quantum circuits and hybrid quantum classical algorithms.

Advanced topics include the design and implementation of quantum neural networks, such as quantum convolutional and graph-based neural networks.

Intended learning outcomes

After passing the course, the student should be able to:

  • explain and describe the basics of quantum computing and quantum machine learning
  • implement and evaluate differentiable quantum programming techniques
  • design and optimise variational quantum circuits for machine learning tasks
  • create and evaluate advanced quantum-based neural network architectures

in order to develop and optimise quantum algorithms for advanced data processing tasks.

Literature and preparations

Specific prerequisites

Knowledge in algebra and geometry, 7.5 higher education credits, equivalent to completed course SF1624.

Knowledge in neural networks, 5.5 higher education credits, corresponding to completed course DD2424/DD2437 or completed modules KON1 and LAB2 in DD2437.

Knowledge and skills in programming covering 6 credits, equivalent to completed course DD1337/DD1310-DD1319/DD1321/DD1331/DD100N/ID1018.

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

A, B, C, D, E, FX, F

Examination

  • LAB1 - Laboratory Assignments, 4.0 credits, grading scale: P, F
  • PRO1 - Project Work, 3.5 credits, grading scale: A, B, C, D, E, FX, 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.

Examiner

Profile picture Stefano Markidis

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

EECS/Computer Science

Main field of study

Computer Science and Engineering

Education cycle

Second cycle

Supplementary information

In this course, the EECS code of honor applies, see:
http://www.kth.se/en/eecs/utbildning/hederskodex