A series of lectures and exercises on:
- Qubits, Quantum Gates and Circuits
- Quantum Machine Learning with Parametrized Quantum Circuits
The course will be offered in P1
Autumn 2023 Start 30 Oct 2023 programme students
25%
Normal Daytime
English
KTH Campus
Places are not limited
Autumn 2023 Start 30 Oct 2023 programme students
51071
Autumn 2023 Start 30 Oct 2023 programme students
Stefano Markidis (markidis@kth.se)
A series of lectures and exercises on:
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:
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.
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).