Название: A Computational Introduction to Quantum PhysicsАвтор: Sølve Selstø
Издательство: Cambridge University Press
Год: 2024
Страниц: 195
Язык: английский
Формат: pdf (true)
Размер: 46.2 MB
This concise textbook introduces an innovative computational approach to quantum mechanics. Over the course of this engaging and informal book, students are encouraged to take an active role in learning key concepts by working through practical exercises. The book equips readers with some basic methodology and a toolbox of scientific computing methods, so they can use code to simulate and directly visualize how quantum particles behave. The important foundational elements of the wave function and the Schrödinger equation are first introduced, then the text gradually builds up to advanced topics including relativistic, open, and non-Hermitian quantum physics. This book assumes familiarity with basic mathematics and numerical methods, and can be used to support a two-semester advanced undergraduate course. Source code and solutions for every book exercise involving numerical implementation are provided in Python and MATLAB, along with supplementary data. Additional problems are provided online for instructor use with locked solutions.
It may be an advantage to have a certain familiarity with classical physics - having a feel for concepts such as kinetic and potential energy, momentum and velocity. Knowing some basic concepts from statistics may come in handy as well. However, mathematics is more important here. In order to practice quantum physics, the proper language and framework is that of mathematics and numerics. You must be familiar with basic calculus and linear algebra - both numerically and analytically. Topics such as differentiation, Taylor expansions, integration, differential equations, vectors, matrix operations and eigenvalues should be quite familiar. The quantities to be calculated and visualized will predominantly be obtained by numerical means. Thus, it is crucial that you have some experience in doing so. This, in turn, requires familiarity with a relevant software environment or programming language, be it Python, MATLAB/Octave, Julia, Java, C/C++ or anything similar.
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