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.

Learning outcomes

Upon successful completion of the course, the student will be able to:

• Explain the fundamental principles of Quantum Machine Learning (QML), including parameterized quantum circuits, hybrid quantum–classical optimization loops, and how these relate to classical machine learning methods such as deep neural networks.
• Encode and transform physical, engineering, and real-world data into QML models, and evaluate models in terms of expressivity, trainability, and potential biases.
• Use Python-based tools for quantum computing, such as Qiskit and PennyLane, and combine them with classical machine learning libraries such as PyTorch or Keras to create practical workflows.
• Apply QML models to real-world problems by designing and executing workflows on simulators and cloud-based quantum processing units (QPUs), while analyzing resource requirements and robustness.
• Carry out a small-scale QML project, including problem formulation, model design, implementation, evaluation, and presentation, while exploring current research and emerging directions in QML.

Content

• Fundamental principles of quantum computing: circuit-based quantum computing and key quantum algorithms.
• Quantum programming interfaces: practical experience with PennyLane and Qiskit on simulators and real quantum processing units (QPUs).
• Classical, quantum, and hybrid machine learning models: overview of key concepts and workflows.
• Variational quantum circuits: methods for data encoding, parameterization, and training strategies.
• Quantum Approximate Optimization Algorithm (QAOA).
• Variational Quantum Eigensolver (VQE).
• Quantum neural networks (QNNs): architecture, training, and applications.
• Quantum annealing and Ising-model-based optimization.
• Quantum ensemble learning and clustering methods.
• Quantum kernel methods: advanced techniques for classification and regression.
• Applications in physics and engineering.

Instruction

Lectures, workshops, laboratory sessions, and project supervision.

Assessment

Written assignments. Written exam. Written project report.

If special circumstances exist, the examiner may make exceptions to the specified assessment method and allow an individual student to be examined in an alternative way. Special circumstances may include, for example, a decision regarding targeted pedagogical support from the university’s coordinator for students with disabilities.