Student rights and obligations (login required)
Linköping University common rules and regulations (in Swedish)
This is an internal version that might contain incomplete information.
Quantum Machine Learning, 6 credits
Kvantmaskininlärning, 6 hp
TSIT06
Main field of study
Computer Science and Engineering, Applied PhysicsCourse level
Second cycleCourse type
Programme courseEducation components
Preliminary scheduled hours: 0 hRecommended self-study hours: 160 h
Available for exchange students
YesECV = Elective / Compulsory / Voluntary
| Course offered for | Semester | Period | Timetable module | Language | Campus | ECV | |
|---|---|---|---|---|---|---|---|
| 6CYYY | Applied Physics and Electrical Engineering, Master of Science in Engineering | 9 (Autumn 2026) | 2 | 1 | Swedish/English | Linköping, Valla | E |
| 6CYYY | Applied Physics and Electrical Engineering, Master of Science in Engineering (Applied Physics - Theory, Modelling and Computation) | 9 (Autumn 2026) | 2 | 1 | Swedish/English | Linköping, Valla | E |
| 6CYYY | Applied Physics and Electrical Engineering, Master of Science in Engineering (Photonics and Quantum Technology) | 9 (Autumn 2026) | 2 | 1 | Swedish/English | Linköping, Valla | E |
The course syllabus is preliminary
Main field of study
Computer Science and Engineering, Applied PhysicsCourse level
Second cycleAdvancement level
A1FCourse offered for
- Master of Science in Applied Physics and Electrical Engineering
Intended learning outcomes
After completing the course the student should be able to:
- use relevant concepts and methods in quantum machine learning to formulate, structure and solve practical problems.
- infer the parameters in a number of common quantum machine learning models.
- evaluate and choose among models.
- implement quantum machine learning models and algorithms in a programming language.
Course content
- Introduction to machine learning, and introduction to quantum computers, a brief introduction to quantum mechanics
- Representation of classical data in quantum systems, coding and embedding, quantum data representation and quantum feature map
- Quantum algorithms for machine learning, quantum classifiers, quantum mechanical kernel methods, quantum clustering
- Quantum variational circuits, quantum neural networks, quantum convolutional neural networks (QCNNs), quantum federated learning (QFL), quantum reinforcement learning (QRL), kvantmekanisk multimodal inlärning
- Research directions in the area
- Applications of QML in language models, computer vision, health care, medicin design, transport, and intrusion detection
Examination
| TEN1 | Written examination | 4 credits | U, 3, 4, 5 |
| LAB1 | Labatory work | 2 credits | U, G |
Grades
Four-grade scale, LiU, U, 3, 4, 5Department
Institutionen för systemteknikCourse literature
Books
- Schuld & Petruccione, (2020) Machine Learning with Quantum Computers Springer
| Code | Name | Scope | Grading scale |
|---|---|---|---|
| TEN1 | Written examination | 4 credits | U, 3, 4, 5 |
| LAB1 | Labatory work | 2 credits | U, G |
Course literature is preliminary.
Books
Schuld & Petruccione, (2020) Machine Learning with Quantum Computers Springer
Note: The course matrix might contain more information in Swedish.
I = Introduce, U = Teach, A = Utilize
| I | U | A | Modules | Comment | ||
|---|---|---|---|---|---|---|
| 1. DISCIPLINARY KNOWLEDGE AND REASONING | ||||||
| 1.1 Knowledge of underlying mathematics and science (courses on G1X-level) |
|
|
X
|
Uses quantum mechanics, linear algebra, discrete mathematics |
||
| 1.2 Fundamental engineering knowledge (courses on G1X-level) |
|
|
|
|||
| 1.3 Further knowledge, methods and tools in any of : mathematics, natural sciences, engineering (courses at G2X level) |
|
|
X
|
Algorithms, error-correcting codes |
||
| 1.4 Advanced knowledge, methods and tools in any of: mathematics, natural sciences, engineering (courses at A1X level) |
X
|
X
|
|
TEN1
LAB1
|
In-depth study of machine learning algorithms and their evaluation, methods from advanced quantum mechanics and information processing |
|
| 1.5 Insight into current research and development work |
X
|
|
|
Current developments in the field, new research directions in the area |
||
| 2. PERSONAL AND PROFESSIONAL SKILLS AND ATTRIBUTES | ||||||
| 2.1 Analytical reasoning and problem solving |
|
X
|
|
TEN1
LAB1
|
Modeling of quantum mechanical systems |
|
| 2.2 Experimentation, investigation, and knowledge discovery |
|
X
|
X
|
LAB1
|
Experiment in a software environment that simulates or uses quantum systems |
|
| 2.3 System thinking |
|
|
|
|||
| 2.4 Attitudes, thought, and learning |
|
|
X
|
Own work in laborations etc. |
||
| 2.5 Ethics, equity, and other responsibilities |
|
|
|
|||
| 3. INTERPERSONAL SKILLS: TEAMWORK AND COMMUNICATION | ||||||
| 3.1 Teamwork |
|
|
X
|
Group laborations |
||
| 3.2 Communications |
|
|
|
|||
| 3.3 Communication in foreign languages |
|
|
X
|
Course literature, lectures, and lab instructions in English. |
||
| 4. CONCEIVING, DESIGNING, IMPLEMENTING AND OPERATING SYSTEMS IN THE ENTERPRISE, SOCIETAL AND ENVIRONMENTAL CONTEXT | ||||||
| 4.1 Societal conditions, including economically, socially and ecologically sustainable development |
X
|
|
|
Effects of new technology |
||
| 4.2 Enterprise and business context |
|
|
|
|||
| 4.3 Conceiving, system engineering and management |
X
|
|
|
Limitations of quantum machine learning |
||
| 4.4 Designing |
|
|
|
|||
| 4.5 Implementing |
|
|
|
|||
| 4.6 Operating |
X
|
|
|
Evaluation of claims in the area |
||
| 5. PLANNING, EXECUTION AND PRESENTATION OF RESEARCH DEVELOPMENT PROJECTS WITH RESPECT TO SCIENTIFIC AND SOCIETAL NEEDS AND REQUIREMENTS | ||||||
| 5.1 Societal conditions, including economically, socially and ecologically sustainable development within research or development projects |
X
|
|
|
Effects of new technology |
||
| 5.2 Economic conditions for research or development projects |
|
|
|
|||
| 5.3 Identification of needs, structuring and planning of research or development projects |
X
|
|
|
Limitations of quantum machine learning |
||
| 5.4 Execution of research or development projects |
|
|
|
|||
| 5.5 Presentation and evaluation of research or development projects |
X
|
|
|
Evaluation of claims in the area |
||
This tab contains public material from the course room in Lisam. The information published here is not legally binding, such material can be found under the other tabs on this page.
There are no files available for this course.