Basic Information
Exact Diagonalization - Construction of The Hamiltonian, Using Symmetries And Conserved Quantities, Lanczos Algorithm. Classical Monte Carlo - Markov Chains and Metropolis Algorithm, Critical Slowing Down, Cluster Updates. Quantum Monte Carlo - Quantum to Classical Mapping and The Sign Problem, World Line Representation, Stochastic Series Expansion ( Sse ), Loop Algorithms. Matrix Product State (mps) Techniques - Entanglement Entropy and Area Law, Density Matrix Renormalization Group (dmrg), Real and Imaginary Time-evolution Methods. Learning Outcomes# Upon Successful Completion of This Course Students Will Become Acquainted With a Variety of Computational Techniques For Classical And Quantum Many-body Problems Relevant to Present-day Research. Students Will Be Able to Analyze a Given Problem and Understand Which Method Would Result in The Most Efficient Simulation and Analysis, Implement The Appropriate Numerical Algorithm, and Analyze The Outcome of The Numerical Simulations Interpreting The Physical Properties of The System Under Study.
Faculty: Physics
|Pre-Academic
Pre-required courses
(118122 - Quantum Mechanics 3 and 118129 - Statistical Mechanics 2)