PHYS 367 - Topics in Scientific Computation

Computational Methods
This course focuses on diverse physical problems and computational techniques that can be applied to them, with an emphasis on the mathematical motivation behind the methods. Problems are drawn from electrodynamics, quantum mechanics, classical mechanics, and special and general relativity. The course develops methods for solving ODEs and PDEs and integrating arbitrary functions in multiple dimensions. Numerical linear algebra is covered in both full and iterative form. Additional topics include nonlinear minimization, Galerkin methods, neural network models, and chaotic dynamics.

Quantum Computation and Computational Quantum Mechanics
The course explores the intersection of computation and quantum mechanics, both how computers are used to solve problems in quantum mechanics and how quantum mechanics can be leveraged to perform computations. The first half of the course is an introduction to quantum computing, covering qubits, quantum circuit diagrams, and examples of quantum algorithms. The second half of the course covers classical algorithms used to analyze many-body quantum systems, including exact diagonalization and quantum Monte Carlo techniques.

Unit(s): 1
Group Distribution Requirement(s): Distribution Group III
Prerequisite(s): MATH 201 , MATH 202 and PHYS 211 (or PHYS 201), and PHYS 202
Instructional Method: Conference
Grading Mode: Letter grading (A-F)
Repeatable for Credit: May be taken up to 2 times for credit if different topics.
Group Distribution Learning Outcome(s):

Use and evaluate quantitative data or modeling, or use logical/mathematical reasoning to evaluate, test or prove statements.
Given a problem or question, formulate a hypothesis or conjecture, and design an experiment, collect data, or use mathematical reasoning to test or validate it.
Collect, interpret, and analyze data.