Lecture and tutorial classes

Machine learning in quantum physics

(Winter term 2018/2019)

Quantum machine learning is an emerging research field. This first course focuses on the subfield where classical machine learning is applied in quantum physics. The goal of this course is to provide the students with the necessary skills to understand the main ideas and some details of the ongoing research.

Content

  1. Introduction to artificial neural networks and deep learning

  2. Applications in quantum physics

Exercises and other files will be uploaded here.

Formal things

Lecture and tutorial class (Übung) have been moved to

  • Tuesday 8:30am, room 25.33.00.61 (lecture)

  • Friday 8:30am, room 25.33.00.61 (tutorial class)

  • Prerequisites for attending are basic knowledge

    • Linear algebra,

    • Calculus, and

    • Quantum mechanics.

  • Assignments (click here) will be uploaded every two weeks. The solutions to the assignment sheets need to be handed in. As a prerequisite for the exam, at least 70% of the assignment sheets need to be finished. There will be no corrections but the solutions will be discussed in the tutorial classes.

  • The final grade will be determined with an oral exam. Potential content for the exam is based on the exercise sheets, the tutorial class, and the lectures. The lecture is mostly but not completely covered by the informal lecture notes.

  • Oral exams: Available time slots are:

    • Thursday, February 14, at 9:30h and 11:45h

    • Thursday, March 28, at 9:30h and 11:45h

More dates/times slots will be made available once the time slots above are full or on request.
Please send a mail including your Matrikelnummer to teaching@mkliesch.eu to fix a time slot. I will then confirm the times slot and forward the information to the Prüfungsamt to make it official.

Preliminary schedule: 30h lecture (VL) and problem classes (Ü)

October (8)

  • 09 | VL (Motivation, course outline, formalities), Exercise 1 handed out

  • 12 | Ü (Introduction, student questions & discussion)

  • 16 | VL (General ML & statistics)

  • 19 | Ü due date: discussion of Exercise 1, Exercise 2 handed out

  • 23 | VL (Basics of probability theory & statistics)

  • 26 | Ü, (Student questions & discussion)

  • 30 | VL Canceled and moved to Friday; exercise sheet 3 will be posted on October 31

November (8)

  • 02 | VL (General ML & statistics); due date: Exercise 2

  • 06 | VL (DL)

  • 09 | Ü (Discussion of exercise 2)

  • 13 | VL (DL); due date: Exercise 3,
    exercise 4 will be handed out on Nov. 14 (1 day late due to sickness)

  • 16 | Ü (Discussion of Exercise 3)

  • 20 | VL (DL)

  • 23 | Ü (Student questions & discussion)

  • 27 | VL (DL); due date: Exercise 4, Exercise 5 handed out

  • 30 | Ü (Discussion of Exercise 4)

December (6)

  • 04 | VL (Quantum many-body systems)

  • 07 | Ü (Student questions & discussion)

  • 11 | VL (Neural networks and phase transitions)

  • 14 | Ü, Due date: Exercise 5, Exercise 6 handed out

  • 18 | VL (Neural networks and phase transitions)

  • 21 | Ü (Student questions & discussion)

January (8)

  • 08 | VL (Finish PTs, start with RBMs)

  • 11 | Ü, Due date: Exercise 6
    Exercise 7 will be handed out only on Thursday, Jan. 10.

  • 15 | VL (RBMs)

  • 18 | Ü (Student questions & discussion)

  • 22 | VL (Generative learning in physics), due date: Exercise 7.1

  • 25 | Ü (Discussion of Exercise 7.1)

  • 29 | VL (Generative learning in physics), due date: Exercise 7.2-7.4

  • 01 | Ü (Discussion of Exercise 7.2-7.4)

References