title: Physical Artificial Neural Networks - Optical and Atomic Approaches to Deep Learning
Creators: name: Hansmann, Kai Niklas
Publisher: name: UNSPECIFIED
Year: 2024

description: In recent years, machine learning techniques have found countless applications in industry, research, as well as everyday life. The realization of such systems relies heavily on the ever-growing capabilities of digital computing systems. However, physical systems, and especially their dynamics, have already proved their potential to be alternative implementation platforms. Especially optical systems have been a focal point of this research with proposals of free-space and integrated photonics optical neural networks.

In this thesis, we aim to aid in the search of new physical implementation platforms of artificial neural networks. Contributing to the field of optical neural networks, we develop a description of temperature-dependent intensity noise suppression in quantum dot superluminescent diodes. Those light sources emit spatially directed radiation, which is high-powered, spectrally broadband and typically subject to thermal fluctuations, described by the central degree of second-order coherence of 2.0. In 2011, the group of Prof. Dr. W. Elsäßer at the Technical University Darmstadt observed that intensity fluctuations are reduced to 1.33 when tuning the running temperature of the diode to 190 K. We show, that this effect can be described via a photon statistics manipulation of the emission due to interaction with the pumped diode material. Therefore, intensity noise suppression in such diodes can be explained as a temperature driven saturation effect.

Furthermore, we go beyond purely optical implementations of artificial neural networks looking at atomic systems as the main focus of potential new machine learning platforms. Specifically, we make use of the movement of thermal atoms in optically shaped potential landscapes as well as harnessing the nonlinear dynamics of coherent matter waves in Bose-Einstein condensates. Considering thermal atoms inside a box potential, we demonstrate that a neuron algorithm can be implemented using optical dipole potentials as input and the measurement of the particle number remaining at the end of the procedure as output. This thermal cloud neuron shows similar features compared to conventional implementations and is able to solve benchmark problems.

Moving even more towards a purely atomic implementation of an artificial neural network, this thesis describes an implementation based on intrinsic nonlinearities of coherent matter waves. There, we present an in-detail description of the four-wave mixing process of plane waves in a homogeneous condensate. Analytical as well as numerical investigations of the dynamics of this process are performed, revealing Josephson-like oscillations within the four-wave mixing states. Interpreting the complex amplitudes of three momentum components of the four-wave mixing process as input, and the fourth one as output, we demonstrate the implementation of a complex-valued neuron. A single realization of such a four-wave mixing neuron is able to solve the benchmark XOR problem. By parallelizing such neurons and setting up communicating layers of a network, we show that nonlinear dynamics of Bose-Einstein condensates can be used to implement an atomic neural network.