Year: 2016
Type: dissertation
Abstract: In this Ph.D. Thesis we investigate the viability of using quantum dot ensembles as a quantum memory architecture through the use numerical simulations to study population transfer within quantum dots. This is followed by an investigation into the effects of high order wavemixing on the population transfer within two level systems, \nwhich was born from effects noted while simulating quantum dots. \n \nWe study the initialisation of an ensemble of inhomogeneously broadened quantum dots, introducing a novel initialisation method utilising pump field with a slow frequency sweep. We focus on the properties of such an initialisation procedure and conclude that the maximum initialisation fidelities are determined entirely by the Zeeman splittings and decay rates of the quantum dots. \n \nWe study several possibilities for performing π rotations on the population of an ensemble of quantum dots, and show the RCAP protocol is the most applicable. We study this protocol in the context of quantum dots and give the optimal parameters to use to generate high fidelity π pulses. \n \nWe then bring together our work on quantum dots population transfer with the work of others covering the write and read procedures on quantum dots to provide a feasibility analysis of the complete quantum memory protocol. \n \nThe work on wavemixing presented in this thesis uses a novel approach to analyse wavemixing effects which is used to predict the population transferred in two level \nsimulations of wavemixing processes. We provide simulation confirmation of our approach to analyse wavemixing effects and then go on to calculate the disruptive effects of wavemixing caused by high intensity lasers on some simple systems. Finally we show that large orders of wavemixing can, at least in principle, be used for coherent \npopulation transfer. \n
Author: Michael Woodhouse
Language: English
Subfield: Atomic and Molecular Physics, and Optics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2299898547