FDD3268 Applied Quantum Machine Learning 5.0 credits

A series of lectures and exercises on:

• Qubits, Quantum Gates and Circuits
• Quantum Machine Learning with Parametrized Quantum Circuits 

The course is divided into two modules:

Module I - Introduction to Qubits, Quantum Gates and Circuits: Introduction to quantum computing, qubits, quantum gates, and quantum circuits.

Module II - Quantum Machine Learning with Parametrized Quantum Circuits: data encoding, training parametrized quantum circuits, variational classification, quantum feature maps, and kernels.

In addition, we discuss the sustainability aspects of quantum machine learning.

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

• Describe and discuss how to develop a machine-learning application using a quantum-parametrized circuit
• Design and implement a machine learning application using quantum machine learning software
• List the differences between classical and quantum approaches for machine learning
• Compare the cost of quantum machine learning with traditional computing regarding power consumption

Knowledge of basic machine learning techniques and linear algebra is required. Experience with Python is required.

Knowledge of basic machine learning techniques and linear algebra is required. Experience with Python is required.

A laptop or desktop computer is required to complete the final project.

The recommended textbook is "Supervised Learning with Quantum Computers" by M. Schuld and F. Petruccione. The book is available online in KTH's electronic library.

If the course is discontinued, students may request to be examined during the following two academic years.

• EXA1 - Examination, 5.0 credits, grading scale: P, 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.

To pass the course, the student must pass an advanced final project (report and presentation).

Stefano Markidis

• 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.