Quantum Machine Learning

Seminar an der Universitšt des Saarlandes, Fachrichtung InformatikLSF 136315

Scope:

Quantum Machine Learning (QML) is an emerging field that aims to leverage the properties of quantum computation to improve traditional machine learning algorithms [1]. This seminar on QML aims to initially show what benefits quantum technologies can provide to the AI area of machine learning. While machine learning algorithms are used to compute massive amounts of data, quantum machine learning employs qubits and quantum operations to improve computational speed and data storage. This includes hybrid quantum-classical methods involving classical and quantum processing, where computationally difficult subroutines are outsourced to a quantum device. These routines can be more complex but executed more efficiently on a quantum computer. Furthermore, such hybrid algorithms can be considered a specific class of machine learning algorithms where the parameters of a quantum algorithm can be tuned using classical optimization procedures to solve typical problems in the context of supervised and reinforcement learning [2, 3].
In this seminar, we will take a closer look at selected methods in the context of quantum machine learning with a particular focus on hybrid quantum-classical algorithms. The seminar type is classic in the sense that registered participants will present assigned topics and discuss the strength and weaknesses of the presented approaches. In addition, there will be two dedicated opponents for each presentation of an assigned topic. Participation in the seminar will be graded; please check the requirements page in this regard.

The seminar counts 7 ECTS credit points (CS).

Prerequisites:

This seminar aims primarily at advanced master students in Computer Science who preferably hold a B.Sc. degree in this or related field. Good knowledge in AI (introductory course on AI), machine learning, and mathematics (in particular linear algebra) is required. Selected background references are given on the topic page and expected to be read and utilized by registered participants as appropriate. Attendance of the seminar without registration (no presentation and certificate) by anyone who is interested in the topics is, of course, very much welcome. The seminar language is English or German depending on the audience.

Date and Location:

The seminar is held on Tuesday from 4:15 pm to 6 pm.

PLEASE NOTE: First session (introduction with topic assignments) on Tuesday 26.4.2022 from 4:15 pm to 6 pm.

Due to the COVID19 pandemic:

  • The seminar takes place virtually in the Web via Zoom unless otherwise stated in the schedule.
  • Each session has an individual online meeting  room address, which will be announced by the seminar organizers on time (see schedule) per E-Mail to all registered participants of the seminar; other interested but not registered parties who like to attend the seminar will be informed on their request.

Please check the seminar schedule frequently for changes.

Registration and Topic Assignments:

Registration request with preferences to the seminar is through the central SIC seminar system until April 12, 2022. Central assignment of students to the seminar will then automatically be done by the system on April 15, 2022. If there are free slots available after that date, students can apply for these slots by e-mail to and decision by the seminar organizers.

Assignment of seminar presentation topics will be done during the first session of the seminar on Tuesday, April 26, 2022, 4:15 - 6pm. It is recommended to carefully check the reference papers and preliminary presentation dates of topics (see schedule) before registering to the seminar in the central SIC seminar system.

References:

[1] Biamonte, J. et al. (2017). Quantum Machine Learning. Nature, 549(7671):195-202.

[2] Benedetti, M. et al. (2019): Parameterized quantum circuits as machine learning models. Quantum Science and Technology 4.4: 043001.

[3] Schuld, M. et al. (2020):  Circuit-centric quantum classifiers. Physical Review A 101.3: 032308.