Quantum Machine Learning

Education facts

Code: 1FA020

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.

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.

Education offerings

Each offering has its own dates and conditions. Closed offerings are retained as history and do not mean that a new application is open.

• Quantum Machine Learning

  Uppsala University

  Uppsala

  Start date: 22 March 2027

  End date: 6 June 2027

  Pace of study: 33 %