Analog Photonics Computing for Information Processing, Inference and Optimisation

TL;DR

This review explores the current state and future prospects of photonics computing, highlighting its applications in optimization, neural networks, and quantum computing, emphasizing efficiency and technological challenges.

Contribution

It provides a comprehensive overview of photonics computing architectures, applications, and challenges, including recent advancements and potential future directions.

Findings

Photonics computing offers high efficiency for optimization and neural network tasks.

Various applications include machine learning, image processing, and probabilistic models.

Technological challenges and prospects for optical quantum computing are discussed.

Abstract

This review presents an overview of the current state-of-the-art in photonics computing, which leverages photons, photons coupled with matter, and optics-related technologies for effective and efficient computational purposes. It covers the history and development of photonics computing and modern analogue computing platforms and architectures, focusing on optimization tasks and neural network implementations. The authors examine special-purpose optimizers, mathematical descriptions of photonics optimizers, and their various interconnections. Disparate applications are discussed, including direct encoding, logistics, finance, phase retrieval, machine learning, neural networks, probabilistic graphical models, and image processing, among many others. The main directions of technological advancement and associated challenges in photonics computing are explored, along with an assessment of its efficiency. Finally, the paper discusses prospects and the field of optical quantum computing, providing insights into the potential applications of this technology.