Physics

This course introduces Scientific Machine Learning, beginning with an overview of traditional and modern machine learning methods illustrated with examples from physics. It then transitions to physics-informed approaches, where physical laws, symmetries, and mechanistic models are embedded into learning frameworks. Tutorials and assignments will emphasize developing programming skills in Python.

This series is a crash course introduction to a handful of advanced topics designed to tackle the general problem of how to engineer Positive Operator-Valued Measures (POVMs) using observable building blocks, the so-called Instrument Manifold Program. This program emerged from a recent fundamental breakthrough: how to realize the measurement of a spin’s direction, a.k.a. the spin-coherent-state POVM, a spherical set of outcomes analogous to the well known coherent-state POVM of the standard phase plane.

Mostly ignore the Introduction content of the Oct 27 lecture (up to 14:14). The content and direction of the course evolved such that different portions of the “Old Schedule” (at 0:27) were covered. The schedule on this page has been corrected and now accurately reflects the content of the lectures.

The “Supplements” on Oct 30 and Nov 06 are actually just Lectures.

Outline:
Oct 27: The Planimeter and the Weyl-Heisenberg Group of Kinematic Transformations
Oct 30: The Planimeter and the Weyl-Heisenberg Group of Canonical Transformations
Nov 03: No Class
Nov 06: Measuring Instruments, Indirect Quantum Measurement, and Positive Gaussian Kraus Operators
Nov 10: Gaussian Channels, Gaussian Random Unitaries, and Sequential Measurements
Nov 13: Wiener Increments and the Ito Rules
Nov 17: No Class
Nov 20: General Gaussian Kraus Operators, Lindblad Operators, and Diffusive Measurement
Nov 24: The Markov Property and The Central Limit Theorem
Nov 27: Instrumental Groups, von Neumann Measurement, and Diffusive Heterodyne
Dec 01: No Class
Dec 04: The Stratonovich Product, The Maurer-Cartan Stochastic Differential, and Indirect Homodyne
Dec 08: The Kraus-Operator Density and The Fokker-Planck-Kolmogorov Equation of a Diffusive Measurement
Dec 11: Simultaneous Diffusive Measurements of Non-Commuting Observables and the Instrument Manifold Program

Location & Building Access: Alice Room, 3rd Floor, Perimeter Institute, 31 Caroline St N, Waterloo (Exception - November 27 in Space Room, 4th Floor)

Registration: Please sign-up here: https://forms.office.com/r/dEA4EUq0CU

Participants who do not have an access card for Perimeter Institute must sign in at the security desk before each session. For information on parking or accessibility please contact [email protected].

The annual Graduate Students’ Conference showcases the diverse research directions at Perimeter Institute, both organized and presented by the students. Our graduate students are invited to share their best work with their fellow PhD students, PSI students and other PI residents interested in hearing about physics research and discussing it in a lively atmosphere full of questions.

The aim of this course is to explore the main ideas of the statistical physics approach to critical phenomena. We will discuss phase transitions, using the ferromagnetic phase transition and the Ising model as our primary example. The renormalisation group approach will be an important part of this course.

This is a theoretical physics course that aims to review the basics of theoretical mechanics, special relativity, and classical field theory, with the emphasis on geometrical notions and relativistic formalism, thus setting the stage for the forthcoming courses in Quantum Mechanics, and Quantum Field Theory in particular, as well as in General Relativity and Quantum Gravity. Instructor: Aldo Riello Students who are not part of the PSI MSc program should review enrollment and course format information here: https://perimeterinstitute.ca/graduate-courses

Machine learning has become a very valuable toolbox for scientists including physicists. In this course, we will learn the basics of machine learning with an emphasis on applications for many-body physics. At the end of this course, you will be equipped with the necessary and preliminary tools for starting your own machine learning projects. Instructor: Mohamed Hibat Allah Students who are not part of the PSI MSc program should review enrollment and course format information here: https://perimeterinstitute.ca/graduate-courses

This course teaches basic numerical methods that are widely used across many fields of physics. The course is based on the Julia programming language. Topics include an introduction to Julia, linear algebra, Monte Carlo methods, differential equations, and are based on applications by researchers at Perimeter. The course will also teach principles of software engineering ensuring reproducible results Instructor: Erik Schnetter/Dustin Lang/Subhayan Students who are not part of the PSI MSc program should review enrollment and course format information here: https://perimeterinstitute.ca/graduate-courses

This course teaches basic numerical methods that are widely used across many fields of physics. The course is based on the Python programming language. Topics include an introduction to Python, linear algebra, Monte Carlo methods, root finding, integration, differential equations, and are based on applications by researchers at Perimeter. The course will also teach principles of software engineering ensuring reproducible results.