Computing with Light and Sound

In contemporary times, intricate challenges, whether they pertain to climate dynamics or economic processes, necessitate the handling of big data and sophisticated intelligent systems. At the core of these systems are digital neural networks that can undergo training. Neural networks based on photonic connections can be an adequate alternative, as they come with a broad bandwidth, high processing speed, and integrability with existing electronic chips. However, so far optical neural networks are mostly based on fixed elements whose connections are difficult to reconfigure. In my project SOUND-PC, I aim at creating an alternative architecture to control the photonic siblings of neural networks and will pioneer the approach of using light-sound interactions for the implementation of an optical neural network. The basis for this project lays in nonlinear optics, optoacoustics, stimulated Brillouin scattering, and optical computing.The main goal of SOUND-PC is to experimentally demonstrate several approaches which use the interaction of traveling acoustic waves with light waves for optical neural networks. This concept has the potential to enable a highly reconfigurable computing architecture in one single optical waveguide that can use inputs in the frequency, spatial or time domain with flexible connection of nodes based on optoacoustic interaction. These approaches can potentially be transferred to the quantum domain and contribute to the new emerging field of quantum neural networks.To achieve the main goal of SOUND-PC, I will harness nonlinear optical effects and optoacoustic interactions to address the following objectives:1) Optoacoustic computing in the frequency domain2) In-network computing realized by a distributed optoacoustic approach3) Mode-processor in the spatial domain via topological-selective Brillouin scattering4) Feasibility study of transferring optical computing architectures to the quantum domain.