Color centers in diamond are attractive candidates for implementing single-atom quantum memories in a quantum network. This thesis describes an approach to build quantum networks nodes based on color centers in diamond. We propose to use a novel single-atom quantum memory, the neutral charge state of silicon vacancy (SiV0), as the building block for future quantum network. The unique combination of long spin coherence times and efficient optical transitions makes SiV0 a promising candidate for such application.

Neural networks are machine learning models whose original design has been vaguely inspired by the structure networks of neurons in human brains. Due to recent technological advances that have enabled fast computations on larger models and more training data, neural networks have found many applications in a growing number of areas of science such as computer vision, natural language processing, and medical imaging.

Abstract:

We review the modeling techniques for mid-infrared quantum cascade lasers based on III/V materials, clarifies or corrects ambiguity concepts such as state-dependent effective mass, the non-parabolic kinetic energy and its modeling, and the polarized waveguide confinement in the mathematical perturbation sense.

Abstract

The spin of electrons in silicon quantum dots has been a promising candidate for qubits for quantum computing applications in recent years, demonstrating long coherence time due to its weak spin-orbit coupling and the existence of stable zero nuclear spin isotopes. However, a fundamental challenge is the degeneracy of the conduction band minima, which is a decoherence source. The realization of atomically flat Si/SiGe heterostructures which can potentially solve the small valley splitting issue in Si/SiGe quantum dots applications motivated this work.