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Physics and Classical / Quantum Information


Admission requirements


Many concepts in physics, specifically the statistical mechanics underpinning of thermodynamics, have proven to have a wider applicability in the science of information. This course explores this interplay between these two fields in the area of machine learning. We discuss machine learning as a generalization of the thermodynamics of spin systems and optimization as the search for a physical ground state. The interplay between physics and machine learning becomes clear in analyzing optimal Deep Neural Networks as those that sit on an edge of chaos phase transition; how the optimal state in Deep Neural Networks saturates the maximal possible information bottleneck bound, and how formally Deep Neural Networks are equivalent to disordered quantum field theories. Alternatively, using machine learning insights for physics, Restricted Boltzmann Machines are shown to be highly efficient variational wavefunctions for densely entangled quantum groundstates. The recent Large Language Models are shown to be equivalent to self-re-inforcing spin systems.

Course Objectives

The course will consist of two parts:

  • The first part of the course will consist of a set of conventional lectures and (graded) assignments;

  • The second part of the course will consist of a set of indepedent research projects with a written report and presentations by the students;


For detailed information go to Brightspace

You will find the timetables for all courses and degree programmes of Leiden University in the tool MyTimetable (login). Any teaching activities that you have sucessfully registered for in MyStudyMap will automatically be displayed in MyTimeTable. Any timetables that you add manually, will be saved and automatically displayed the next time you sign in.

MyTimetable allows you to integrate your timetable with your calendar apps such as Outlook, Google Calendar, Apple Calendar and other calendar apps on your smartphone. Any timetable changes will be automatically synced with your calendar. If you wish, you can also receive an email notification of the change. You can turn notifications on in ‘Settings’ (after login).

For more information, watch the video or go the the 'help-page' in MyTimetable. Please note: Joint Degree students Leiden/Delft have to merge their two different timetables into one. This video explains how to do this.

Mode of instruction

See Brightspace

Assessment method

The final course grade will be determined from

  • Graded assignments (50%)

  • Project Presentation (20%)

  • Project report (30%)

Reading list

All materials (this syllabus, problem assignments, background material) can be found on the Leiden University Brightspace site: Brightspace


Enrollment is through MyStudyMap. Registration for the spring semester opens in December. Please see this page for more information.

Please note that it is compulsory to both preregister and confirm your participation for every exam and retake. Not being registered for a course means that you are not allowed to participate in the final exam of the course. Confirming your exam participation is possible until ten days before the exam.

Extensive FAQ's on MyStudymap can be found here.


Lecturers: Prof.dr. K.E. Schalm Dr. D.F.E. Samtleben Dr. M. Schaller