LEARNING OUTCOMES
Understanding general principles underlying, and developing insights into, some select topics from quantum machine learning is a major goal of the course. At the end of the course, the students are expected to be able to understand the design principles underlying quantum learning methods, and be able to critique the latest research on these methods.
The exact content, structure, and organization of the course is evolving, and would be conveyed at the time of registration. Previously, the course has been piloted as a project-based course (2022), and a reinforcement quantum learning seminar (2023).
Credits: 5
Schedule: 05.09.2024 - 17.10.2024
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): Vikas Garg
Contact information for the course (applies in this implementation):
Instructors can be contacted via email. In general, for quick follow-up, we encourage writing (Cc'ing) to all the instructors.
(1) Vikas Garg: vikas.garg@aalto.fi
(2) Alison Pouplin: alison.pouplin@aalto.fi
(3) Adrian Muller: adrian.muller@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
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
applies in this implementation
This particular course offering will be organised as a seminar on Quantum Generative Models. The first two sessions will consist of introductory lectures by the course instructors on generative models and quantum mechanics.
Each of the remaining five sessions will consist of 3 presentations by students (a team of two students per presentation). Students will form pairs and choose from a suggested list of papers according to a predefined schedule.
For each attended session it is mandatory to submit a learning diary, to track attendance and allow to reflect on the learnings. Each student participant is allowed at most one absence.
Assessment Methods and Criteria
valid for whole curriculum period:
- Course project (research, implementation, or literature survey)
- Presentation
- Report
applies in this implementation
To accommodate high demand for the course, there would be no course project this time. Student participants will be evaluated for their presentation according to the following rubric.
(1) Subject knowledge: 0-5 points
(2) Audience engagement: 0-5
(3) Background effort: 0-5
(4) Clarity of presentation: 0-5
(5) Q&A: 0-5
In addition, for exceptional presentations, the instructors may award up to 5 bonus points.
The individual diaries would be required to be turned in within two days of each session (i.e., by Saturday 4pm). The goal of these diaries is to encourage self-reflection; so while turning in the diary is mandatory, incomplete/inaccurate understanding will not be penalised as long as students make an honest effort.
Workload
valid for whole curriculum period:
- Regular (weekly/biweekly) project-specific meetings
- Some meetings may have to take place online, though in-person meetings would be prefered whenever possible.
- Besides some introductory lectures, most of the course would focus on project work.
applies in this implementation
The course will be conducted in regular classroom mode this year. No remote/offline participation can be accommodated.
DETAILS
Study Material
valid for whole curriculum period:
Pointers to relevant literature in quantum computing and quantum machine learning shall be provided. Students might have to read additional material specific to their project.
Substitutes for Courses
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Language: English
Teaching Period: 2024-2025 Autumn I
2025-2026 Autumn IRegistration:
The intake for this course is limited to at most 20 students. We will take several factors into consideration for student selection including, prior studies, mathematical maturity, programming experience, major of study, motivation and the multidisciplinarity and diversity of the student group.
applies in this implementation
The instructors have decided to make an exception this year by increasing the intake for the course to at most 30.
Each presentation should be structured to last no more than 25 minutes (15-20 minutes for presenting material followed by 5-10 minutes of Q&A).
The pairings and paper selections must be conveyed to the instructors over email by 11:59pm Sunday, September 8, 2024. Each pair should suggest 4 papers that they would like to present in decreasing order of their preference, and the instructors would get back shortly afterwards with the paper assigned to them for presentation from this list.
Details on the schedule
applies in this implementation
The course meetings will take place every Thursday from 14:15-16:00 in T4 (i.e., hall A-140 in the Computer Science building).