The goal of the course is to introduce relativistic quantum field theory as the conceptual and mathematical framework describing fundamental interactions. ...
This course provides an overview of key advances in continuous optimization and statistical analysis for machine learning. We review recent learning formulations and models as well as their guarantees
This course gives an introduction to transducers by both considering fundamental principles and their application in classical and quantum systems. The course builds up on the fundamental concept of coupling of two subsystems as a mean to use one subsystem ...
Introduction to the path integral formulation of quantum mechanics. Derivation of the perturbation expansion of Green's functions in terms of Feynman diagrams. Several applications will be presented, including non-perturbative effects, such as tunneling an ...
To introduce several advanced topics in quantum physics, including
semiclassical approximation, path integral, scattering theory, and
relativistic quantum mechanics ...
Series of lectures covering the physics of quantum heterostructures, dielectric microcavities and photonic crystal cavities
as well as the properties of the main light emitting devices that are light-emitting diodes (LEDs) and laser diodes (LDs). ...
We explore the intersection of quantum technologies and sustainability. Topics: 1)discussions of fundamental aspects of thermodynamics of computation; 2)analysis and benchmarks of energy consumption in current and near term real quantum devices; 3)projects ...
Building up on the basic concepts of sampling, filtering and Fourier transforms, we address stochastic modeling, spectral analysis, estimation and prediction, classification, and adaptive filtering, with an application oriented approach and hands-on numeri ...
The goal of the course is to introduce relativistic quantum field theory as the conceptual and mathematical framework describing fundamental interactions such as Quantum Electrodynamics. ...