Quantum and photonic information processing with non-von Neumann architectures

Abstract

Computing and information processing are currently undergoing evolution and change. The development of alternative and non-traditional computing architectures has become increasingly necessary to keep pace with application-specific computing or information security demands, to compensate for the growing obsolescence of Moore's Law, and to efficiently solve previously intractable problems. In this thesis, I present experimental and computational research in the intersections of photonics, quantum information, and machine learning in three categories of non-von Neumann architectures: quantum networks, quantum computers, and photonic computers. Quantum networks use quantum information transfer protocols to achieve information theoretically secure communication. I present simulations, designs, and experimental results for improvements in an on-chip cavity QED and nanophotonics-based platform for quantum network nodes harnessing the light-matter interactions between diamond and silicon-vacancy centers. Photonics-based computers have a multitude of applications, of which two include remote sensing and efficiently solving NP-hard problems. I address both applications, presenting a silicon photonics and ring-resonator based platform for optical beam steering and XY model simulations. Lastly, quantum computers allow for the development of quantum algorithms which can show better algorithm complexity for many computational tasks. I introduce an augmentation of the popular combinatorial optimization quantum algorithm QAOA and show improved performance over a competing algorithm at a small number of independent parameters.