Quantum Machine Learning

Enrollment: Register for MA3001 (and not TMA4310) in Studentweb.

Combining weekly lectures with seminar-style presentations by participants, the course explores how concepts from quantum computing can enhance, accelerate, or fundamentally reshape methods from machine learning, thereby introducing students to the central ideas of Quantum Machine Learning (QML).

The course is primarily intended for 3rd-year and MSc mathematics students and requires only basic familiarity with linear algebra. Although it builds on material from the previous quantum computing course, all essential background in both quantum computing and machine learning will be reviewed in the opening lectures.

First organisational meeting: Wednesday, 7 January 2026, 09:00-10:00 Room: 656, SBII

Central references for the course will be:

1) Schuld, Petruccione (2021). Machine Learning with Quantum Computers, Springer

2) Du, Wang, Guo, Yu, Qian, Zhang, Hsieh, Rebentrost, Tao (2025). A Gentle Introduction to Quantum Machine Learning, Springer

3) Jacquier, Kondratyev (2024). Quantum Machine Learning and Optimisation in Finance, Packt

4) Buchmann (2024). Introduction to Quantum Algorithms, AMS,

5) Nannicini (2020). An Introduction to Quantum Computing, without the Physics, SIAM Review, 62:4, 936-981.

6) Roffe (2019). Quantum error correction: an introductory guide, Contemporary Physics, 60:3, 226-245.

7) Higham, Higham (2019). Deep Learning: An Introduction for Applied Mathematicians, SIAM Review 61:4, 860–891.

8) Hofmann, Schölkopf, Smola (2008). Kernel methods in machine learning, The Annals of Statistics, 36:3, 1171-1220.

3 hrs / week:

Mondays 12.15-14.00 (Simastuen 656, SB2)
Tuesdays 12.15-14.00 (Simastuen 656, SB2)

Mandatory assignment: At least one in-class presentation.

Exam: Oral examination, Week 23

Kurusch Ebrahimi-Fard - kurusch.ebrahimi-fard -@- ntnu.no

Fride Straum - fride.straum -@- ntnu.no