Courses and Lecturers

Random quantum circuits, chaos and quantum thermalization

John Chalker, Oxford University, United Kingdom

Topics of the lectures: Introduction. Random matrix theory. Random quantum circuits. Operator dynamics. Entanglement spreading. Spectral correlations.

Lecture's material: https://sdrive.cnrs.fr/s/3wMpKsXoLaQNAe5

Statistical physics of synthetic quantum matter

Marcello Dalmonte, The Abdus Salam International Center for Theoretical Physics (ICTP), Italy

Topics of the lectures: These lectures explore the rich, bidirectional interplay between statistical physics and synthetic quantum matter – engineered quantum systems that offer unprecedented control and measurement at the single-quantum level.

 Part 1: Synthetic Matter as a Statistical Physics Laboratory: We will discuss how synthetic quantum platforms provide unique testbeds for probing fundamental questions in non-equilibrium quantum many-body dynamics. After a brief overview of the field and recent breakthroughs, we will focus on programmable Rydberg atom arrays. We will illustrate their power by examining their use in realizing and studying lattice gauge theories, a cornerstone of statistical physics.

 Part 2: Statistical Physics Tools for Quantum Engineering: Reversing the perspective, we will demonstrate how methodologies rooted in statistical physics provide essential diagnostic and interpretative tools for synthetic quantum matter. Specifically, we will explore how unsupervised machine learning techniques act as powerful probes for complex many-body states and dynamics. Furthermore, we will discuss how network-theory approaches offer interpretable frameworks for characterizing the intricate correlation structures and performance landscapes emerging in quantum simulators and processors.

Coarse-grained models 

Thomas Franosch, Innsbruck University, Austria

Topics of the lectures: Correlation functions in statistical physics. Linear response. Scattering experiments. Mathematical properties of correlation functions. Representation theorems.

Lecture's material: https://sdrive.cnrs.fr/s/HQJG8M9b8fAe9TY

Active and soft matter 

Sabine Klapp, Technische Universitaet Berlin, Germany

Topics of the lectures: overview soft and active matter. Langevin and Fokker Planck equations. Dynamical density functional theory and applications (passive systems). Active matter models, hydrodynamic theories for flocking and mesoscale turbulence, nonreciprocal polar systems.

Lecture's material: https://sdrive.cnrs.fr/s/eAoM4HE4ysbRTAG

Fluctuations in small systems: from theory to biological applications

Felix Ritort, University of Barcelona, Spain

Topics of the lectures: Fluctuation theorems: basics. Applications of fluctuation theorems in biophysics. Thermodynamics of information and the Maxwell demon. Entropy production in biology.

Keywords: Nonequilibrium fluctuations, single molecule experiments, biophysics, entropy production.

Stochastic processes and extreme values statistics  

Gregory Schehr , Sorbonne University, France

Topics of the lectures: These lectures will provide an introduction to key topics in stochastic processes and extreme value statistics. They will cover fundamental concepts such as random walks, Brownian motion, first-passage times, as well as the statistics of extremes in correlated systems. The focus will be on analytical approaches and universal results, with examples drawn from statistical physics and related fields.

Complex networks

M. Ángeles Serrano, University of Barcelona, Spain 

Topics of the lectures:  What is a complex system? Networks: a change of paradigm. Opportunities and Challenges.

Basic network representations: unweighted/weighted, undirected/directed, unipartite/bipartite, singlelayered/multilayered. Mathematical and computational encodings of networks. Basic network metrics: global, local, and mesoscopic properties. Basic network models: Erdos-Renyi, Configuration Model, Watts-Strogatz, Barabasi-Albert. Dynamical processes: the Voter model; epidemic spreading.

Distances in complex networks. Spatial random graphs. Hyperbolic geometry and the S1/ H2 model. Network maps and embedding techniques. I. The problem of dimensions. Dimensionality of real networks. Dimensional reduction, multidimensional hyperbolic maps. II. The problem of scales. Geometric renormalization: coarse-graining and fine-graining; self-similarity of real multsicale networks; self-similar evolution of real networks. Scaled down and scaled up network replicas. 

Evolutionary dynamics and ecology

Corina Tarnita, Princeton University, US 

Anderson localization in sparse random graphs

Marco Tarzia,  Sorbonne University, France

Topics of the lectures: These lectures will be focused on Anderson localiation on sparse random graphs. A tentative schedule: Brief introduction to the key features of Anderson localization; Properties of the resolvent and its connection to spectral statistics; Self-consistent equations for the resolvent on sparse random graphs and their solutions; Connections between Anderson localization on sparse random graphs and the freezing transition of directed polymers in random media; Outlook, open problems, and conclusions.

Lecture's material: https://sdrive.cnrs.fr/s/DFri5MT3eM7FfCK

Statistical physics of learning and optimization

Riccardo Zecchina, Bocconi University, Italy

Damien Barbier, Bocconi University, Italy

Topics of the lectures: Statistical mechanics and optimization in high dimensions (historical perspective). Some recent advances: the Overlap Gap Property and applications of statistical physics to crypto. Statistical mechanics of neural networks (historical perspective) and why did we miss the deep learning revolution. How to probe out-of-equilibrium states in neural networks? Local entropy and learning. On connected subdominant states (Damien Barbier). Dynamical deep learning and role of physics in modelling AI

Lecture's material (Damien Barbier): https://sdrive.cnrs.fr/s/fkxZf6SoeoJ4aEt