Syllabus

LEARNING OUTCOMES

This is a project-based course. Understanding some general principles of, and developing insights into, quantum computing and/or quantum machine learning is a major goal of the course. At the end of the course, the students are expected to be able to design quantum learning methods and circuits for some classical machine learning tasks or new problem settings, and/or implement and apply quantum machine learning methods on real datasets. 

Credits: 5

Schedule: 05.09.2022 - 08.12.2022

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Vikas Garg, Alexandru Paler

Contact information for the course (applies in this implementation):

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

• valid for whole curriculum period:

  Various topics of interest from quantum computing and quantum machine learning, inlcuding but not limited to,

  •     Adiabatic Computing   
  •     Hybrid Methods
  •     Generative Models (e.g., GANs, VAEs, etc.)
  •     Quantum Sampling Methods
  •     Quantum analogues of machine learning problems (clustering, classification, reinforcement learning, etc.)
  •     Applications of Quantum Methods (in Quantum Chemistry, NLP, Drug Discovery, Finance, etc.)
  •     Theory including information bottleneck, generalization, barren plateaus, lower bounds, quantum speedup, inductive bias
   

Assessment Methods and Criteria

• valid for whole curriculum period:

  • Course project (research, implementation, or literature survey)
  • Presentation
  • Report

Workload

• valid for whole curriculum period:

  -- Regular (weekly/biweekly) project-specific meetings
  -- Some meetings may have to take place online (e.g., because of the evolving COVID situation), though in-person meetings would be prefered whenever possible.
  ---  Besides some introductory lectures, most of the course would focus on project work. 

DETAILS

Substitutes for Courses

• valid for whole curriculum period:

  Substitutes for Courses

Prerequisites

• valid for whole curriculum period:

  Prerequisites

FURTHER INFORMATION

Further Information

• valid for whole curriculum period:

  Each project might have to be assigned to small groups of 2-4 students, depending on factors such as the complexity of the project, total number of enrolled students, etc.  

  Teaching Language : English

  Course that may be completed several times

  Teaching Period : 2022-2023 Autumn I
  2023-2024 Autumn I

  Enrollment :

  The course is open to students having diverse backgrounds (including computer science, physics, math, engineering, etc.), so selection would take into account criteria such as prior preparation, research interests, and academic performance. 

Description