Quantum Machine Learning
Course, Master's level, 1FA020
Expand the information below to show details on how to apply and entry requirements.
Spring 2027 Spring 2027, Uppsala, 33%, On-campus, English
- Location
- Uppsala
- Pace of study
- 33%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 22 March 2027–6 June 2027
- Language of instruction
- English
- Entry requirements
-
120 credits in science/engineering. In addition, one of the following alternatives:
1) Quantum Physics or Quantum Physics F.
Programming Technology I or Introduction to Computational Science.
Linear Algebra II.
2) Introduction to Quantum Computers and Quantum Programming.
Proficiency in English equivalent to the Swedish upper secondary course English 6.
- Selection
-
Higher education credits in science and engineering (maximum 240 credits)
- Fees
- If you are not a citizen of a European Union (EU) or European Economic Area (EEA) country, or Switzerland, you are required to pay application and tuition fees.
- First tuition fee instalment: SEK 13,750
- Total tuition fee: SEK 13,750
- Application deadline
- 15 October 2026
- Application code
- UU-63007
Admitted or on the waiting list?
- Registration period
- 8 March 2027–21 March 2027
- Information on registration from the department
Spring 2027 Spring 2027, Uppsala, 33%, On-campus, English For exchange students
- Location
- Uppsala
- Pace of study
- 33%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 22 March 2027–6 June 2027
- Language of instruction
- English
- Entry requirements
-
120 credits in science/engineering. In addition, one of the following alternatives:
1) Quantum Physics or Quantum Physics F.
Programming Technology I or Introduction to Computational Science.
Linear Algebra II.
2) Introduction to Quantum Computers and Quantum Programming.
Proficiency in English equivalent to the Swedish upper secondary course English 6.
Admitted or on the waiting list?
- Registration period
- 8 March 2027–21 March 2027
- Information on registration from the department
About the course
The course introduces Quantum Machine Learning (QML) and explores how quantum computing can be combined with classical machine learning methods to solve problems in physics and engineering. You gain hands-on experience developing and training quantum and hybrid models using tools such as Qiskit, PennyLane, and classical ML libraries, and running them on simulators and real quantum hardware.
The course covers topics including variational quantum circuits, quantum neural networks, quantum kernel methods, QAOA, VQE, and quantum annealing, and culminates in a practical QML project.
Reading list
No reading list found.
Contact
- Study counselling
- studievagledare@physics.uu.se
- +46 18 471 32 45