| UNITOV Prof. G. M. de Divitiis | QUANTUM MECHANICS FOR ASTROPHYSICS (S1, compulsory, 6 ECTS) |
| Learning Outcomes | Students must gain a thorough understanding of quantum mechanics and its basic principles. In particular, at the end of the course, they will have to be familiar with: 1) density matrix 2) entanglement 3) path integral 4) relativistic quantum mechanics. |
| Knowledge and Understanding | In addition to knowing the topics listed in the learning outcomes, students will also need to understand how these are constructed starting from the basic principles of quantum mechanics and the empirical data. The verification of knowledge and understanding is done through practical and theoretical, written and, if required, oral tests. |
| Applying Knowledge and Understanding | Students will acquire the ability to put into practice the knowledge acquired to solve quantum mechanical problems related to the topics listed in the learning outcomes. |
| Prerequisites | Basic knowledge of quantum mechanics |
| Program | Fundamental principles of quantum mechanics and understanding thereof. Density Matrix and its properties. Entanglement, the EPR paradox and the Bell’s inequalities. Path integral and its use for the semiclassical expansion and the classical limit. Relativistic wave functions. |
| Description of how the course is conducted | The course includes classes using slides and blackboard. Educational material will also be provided for further study after the classes |
| Description of the didactic methods | Although optional, it is highly recommended to attend the classes and interact with the teacher, if required. |
| Description of the evaluation methods | The course assessment includes a written test in which, through some questions, the knowledge and understanding of the topics covered and the ability to put them into practice to solve quantum mechanical problems will be verified. If requested, students will take an oral test. |
| Adopted Textbooks | Notes of the course Description of the Methods and Criteria for Assessing Learning |
| Recommended readings | |
