LEARNING OUTCOMES
This course serves as an introduction to essential foundational tools utilized in
both near-term and fault-tolerant quantum devices. At the end of this course, participants will have the ability to apply these concepts to the development of advanced quantum machine learning algorithms. Additionally, participants will gain the skills to recognize opportunities within the field of machine learning where quantum resources can be effectively employed.
Credits: 3
Schedule: 23.10.2023 - 10.12.2023
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): Ahmad Farooq, Simo Särkkä
Contact information for the course (applies in this implementation):
Ahmad Farooq (ahmad.farooq@aalto.fi)
CEFR level (valid for whole curriculum period):
Language of instruction and studies (applies in this implementation):
Teaching language: English. Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
Content
applies in this implementation
Prerequisites: The course is intended for Master/PhD students who are expected to have basic knowledge of linear algebra, probabilistic theory, and machine learning. This course aims to guide you through fundamental concepts in quantum machine learning, including quantum data encoding, quantum Fourier transform, quantum phase estimation, Hamiltonian simulation and parameterized quantum circuits. A couple of introductory lectures reviewing quantum states, quantum gates, quantum measurements and unitary evolution will be given.
Lectures: Lectures will be given every Monday 14.00-16.00.
Assessment Methods and Criteria
applies in this implementation
Evaluation: In every seminar talk, there is the author who writes a 2-page summary paper about the subject and gives the talk, and an opponent, whose task is to make questions and stimulate discussion after the presentation. Every student is the author for one talk and the opponent for another talk. The author should send the summary paper to the opponent and to the teachers at Thursday preceding the presentation day. The slides should be sent to the teacher latest on the day preceding the presentation. The opponent prepares at least 2-3 questions about the topic to stimulate discussion after the talk. Normal talk is 20-30 minutes.
Active participation in the seminars, seminar presentation and 2-pages written report on the chosen paper. A short list of pre-selected topics will be provided, but students are welcome to propose their own topics, however, the topics need to be approved before.
The grade is pass/fail. To pass the course, attendance to all lectures/seminars is compulsory.
DETAILS
Study Material
applies in this implementation
Samples of proposed topics: The following constitute a sample of topics that will be covered in the seminar. However, students are encouraged to come up with their own topic:
- Quantum state preparation [1, 4].
- Quantum Fourier transform [2, 5].
- Quantum phase estimation [2, 5].
- Variational quantum algorithms [3, 4]
- Hamiltonian simulation [2, 4].
[1] Möttönen, M., Vartiainen, J.J., Bergholm, V., Salomaa, M.M.: Transformation of quantum states using uniformly controlled rotations. Quantum Inf. Comput. 5(467) (2005).[2] M. Nielsen and I. Chuang, Quantum Computation and Quantum Information, Cambridge Series on Information and the Natural Sciences (Cambridge University Press, Cambridge, 2000).
[3] Cerezo, M. et al. Variational quantum algorithms. Nat. Rev. Phys. 3, 625–644 (2021).
[4] M. Schuld and F. Petruccione, Machine Learning with Quantum Computers (Springer, 2021).
[5] Kaye, P., Laflamme, R. & Mosca, M. An Introduction to Quantum Computing (Oxford Univ. Press, 2007)
Substitutes for Courses
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period: