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Data assimilation is a mathematical approach employed by leading operational weather forecast centers worldwide. Its primary purpose is to estimate the most accurate atmospheric state and its associated uncertainties by integrating observations and short-range forecasts. Data assimilation methodologies extend beyond weather prediction; they play a crucial role in monitoring and comprehending climate and its variability through reanalysis of historical data. This versatile tool is instrumental in investigating processes within the domains of Land, Atmosphere, and Oceans.The field of Data Assimilation is unfortunately constrained by a scarcity of well-trained scientists and researchers familiar with its theoretical foundations and practical applications. To address this gap, we are organizing a two-week workshop with a specific focus. This workshop aims to provide comprehensive training by introducing fundamental data assimilation theory and facilitating hands-on sessions, particularly f...

The second annual SciComm Collider workshop will bring together a group of the most innovative science communicators helping to connect the public with topics in physics and astronomy for a three-day workshop aimed at sharing ideas, creating new collaborations, and exploring ways to more effectively engage the public with the most exciting ideas in science. The workshop will consist of short seminars, interactive sessions, and opportunities to brainstorm new ideas with fellow communicators and creators, as well as venues for interaction between invited science communicators and Perimeter outreach/communications team members and researchers.

The workshop marks the halfway point of the similarly named (FoQaCiA, pronounced "focaccia") collaboration between researchers in Canada and Europe, funded as part of a flagship partnership between NSERC and Horizon Europe.

https://www.foqacia.org/

The goal of FoQaCiA is to develop new foundational approaches to shed light on the relative computational power of quantum devices and classical computers, helping to find the "line in the sand" separating tasks admitting a quantum speedup from those that are classically simulable.

The workshop will focus on the four central interrelated themes of the project:
1. Quantum contextuality, non-classicality, and quantum advantage
2. The complexity of classical simulation of quantum computation
3. The arithmetic of quantum circuits
4. The efficiency of fault-tolerant quantum computation

Our view is that the future success of quantum computing critically depends on advances at the most fundamental level, and that large-scale investments in quantum implementations will only pay off if they can draw on additional foundational insights and ideas

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Scientific Organizers:

Rui Soares Barbosa (INL - International Iberian Nanotechnology Laboratory)
Anne Broadbent (University of Ottawa)
Ernesto Galvão (INL - International Iberian Nanotechnology Laboratory)
Rob Spekkens (Perimeter Institute)
Jon Yard (Perimeter Institute)

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FoQaCiA is funded by:

    
 

The Standard Model (SM) of particle physics has been very successful in numerous tests over the past several decades. However, the SM can not be the ultimate theory to explain all the fundamental interactions in the Universe because of critical gaps. The neutrino sector is one of the few places where the physics beyond standard has been confirmed through the observation of neutrino oscillations, a phenomenon which cannot occur with the massless neutrinos predicted by the standard model. Neutrinos are elementary particles with spin ½, zero electric charge, and tiny but non-zero masses. They come in three distinct flavors and can oscillate among themselves as they propagate. Since they only feel the weak force, their cross section for interacting with matter is extremely small. In collider experiments, they can only be identified as “missing energy.” Consequently, large specialized experiments had to be built for studying neutrinos. These neutrino experiments have evolved from single-pur...

The course centers on an in-depth study of the symmetry structure of General Relativity and how this is intimately related to its dynamics and to the challenges posed to its quantization. To achieve this understanding, we will introduce a host of concepts and techniques, broadly (and loosely) known under the name of “Covariant Phase Space Method”. This provides a different perspective on GR’s physics, a perspective in which phase space, rather than spacetime, is front and center. We will apply these ideas and techniques to discuss the so-called Problem of Time, Wald's approach to black hole entropy as a Noether charge, and the relationship between Dirac's Hypersurface Deformation Algebra and GR's symmetries and dynamics. We will also discuss the problem of detecting single gravitons as well as crucial analogies and differences between a quantum electromagnetic and gravitational field. Lecture notes specific for the course will be provided.

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.

This course will introduce some advanced topics in general relativity related to describing gravity in the strong field and dynamical regime. Topics covered include properties of spinning black holes, black hole thermodynamics and energy extraction, how to define horizons in a dynamical setting, formulations of the Einstein equations as constraint and evolution equations, and gravitational waves and how they are sourced.

We will discuss mathematical aspects of classical and quantum field theory, including topics such as: symplectic manifolds and the phase space, symplectic reduction, geometric quantization, Chern-Simons theory, and others.

Classical probability theory makes the (mostly, tacit) assumption that any two random experiments can be performed jointly.  This assumption seems to fail in quantum theory.  A rapidly growing literature seeks to understand QM by placing it in a much broader mathematical landscape of ``generalized probabilistic theories", or GPTs,  in which incompatible experiments are permitted.   Among other things, this effort has led to  (i) a better appreciation that many "characteristically quantum" phenomena (e.g., entanglement)  are in fact generic to non-classical probabilistic theories, (ii) a suite of reconstructions of (mostly, finite-dimensional) QM from small packages of assumptions of a probabilistic or operational nature, and (iii) a clearer view of the options available for generalizing QM.  This course will offer a survey of this literature,  starting from scratch and concluding with a discussion of recent developments. 

Mathematical prerequisites: finite-dimensional linear algebra, ideally including tensor products and duality, plus some exposure to category theory (though I will briefly review this material as needed).  

Scheduling note: There will be 5 lectures from March 12-26, then a gap of two weeks before the final 2 lectures held April 16 & 18.

Format: In-person only; lectures will be recorded for PIRSA but not live on Zoom.