Attend this comprehensive school designed for graduate students and junior researchers to master modern numerical methods for studying cooperative phenomena in condensed matter and statistical physics. Learn fundamental programming techniques and advance to sophisticated topics covering dynamics of complex classical and quantum systems, disorder effects, and strong electron correlations. Explore the integration of many-body techniques from solid-state physics, quantum gases, and biological system physical chemistry through lectures by leading experts including Ali Alavi, Michele Ceriotti, Werner Krauth, and Steve White. Master essential computational methods including molecular dynamics for planetary interior exploration, Density Matrix Renormalization Group (DMRG) techniques, quantum transport numerics, Monte Carlo algorithms, and path-integral simulations. Develop expertise in quantum many-body systems through Full Configuration Interaction Quantum Monte Carlo, stochastic series expansion methods, and exact diagonalization approaches. Gain practical experience with high-performance computing, scientific Python programming, and specialized software like ALPS for strongly correlated systems. Study advanced topics such as many-body perturbation theory, quantum complexity theory, finite-size scaling methods, and quantum computer simulations of correlated materials. Build foundational knowledge in statistical mechanics through classical lattice spin models, critical point analysis, and out-of-equilibrium quantum simulations while learning to model quantum nuclear effects and understand materials patterns at the molecular level.

Syllabus

Blackboard Molecular Dynamics
Exploring Planetary Interiors with Molecular Dynamics
Preliminaries for DMRG: An Exact Diagonalization, Quantum Information
Matrix Product States and DMRG
Numerical Quantum Transport: Introduction to Numerics for Quantum Transport
Strong-coupling Impurity Solvers for Electron-phon Problems
Extended DMFT and GW+DMFT
Hybridization Expansion and Non-crossing Approximation
Foundation of Parallel Systems for High-Performance Computing
Efficient simulations of low-dimensional systems - Lecture 1
Efficient simulations of low-dimensional systems - Lecture 2
Modeling the Quantum Nature of Atomic Nuclei by Imaginary Time Path Integrals - Lecture 1
Representing and understanding patterns in materials and molecules - Lecture 3
Introduction to Monte Carlo Algorithms
Hard Disks: From Classical Mechanics to Statistical Mechanics
Stochastic Series Expansion Method for Simulations of Quantum Spins
Ground-state Projection of Quantum Spins in the Valence Bond Basis
Full Configuration Interaction Quantum Monte Carlo - Lecture 1
Full Configuration Interaction Quantum Monte Carlo - Lecture 2
Sampling and Integration: From Gaussians to Maxwell and Boltzmann
Classical Lattice Spin Models: Ising Model, XY Model
Systematic Finite-size Scaling Methods for Analyzing Critical Points
Out-of-Equilibrium Quantum Monte Carlo Simulation and Quantum Annealing
Full Configuration Interaction Quantum Monte Carlo - Lecture 3
Introduction to path-integral Monte Carlo in continuous space - Lecture 1
Introduction to path-integral Monte Carlo in continuous space - lecture 2
Simulating Strongly Correlated Systems with ALPS - Lecture 1
Excited States From Many Body Perturbation Theory
Total Energies From Many Body Perturbation Theory
Simulating correlated materials on quantum computers - Lecture 1
Simulating correlated materials on quantum computers - Lecture 3
Introduction to (Quantum) Complexity Theory
Tutorial: Scientific Python 1
Introduction to Exact Diagonalization
Exact Diagonalization: Symmetries, Dynamics
Reflecting on the P in HPC a Condensed Matter Physics Perspective
Exact Diagonalization: Applications