Analog Photonics Computing for Information Processing, Inference, and Optimization

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.

Analog spin simulators: How to keep the amplitude homogeneous

“…(1) and (2) will generally not match. This critical problem, "broken mapping due to amplitude heterogeneity" [8,10] is known to ultimately invalidate the spin solution [27] and the simulation schemes.…”

“…There has been much interest to simulating such spin graphs especially with optics, because of their ease of access and ability to operate away from equilibrium [8][9][10][11].…”

Grand challenges in neuromorphic photonics and photonic computing

“…In this scenario, photonics is emerging as a promising technology to enable the sustainable development of high-performance computing (HPC) Kitayama et al (2019). Not only can photonics provide fast and high-capacity interconnections among distributed processors Thraskias et al (2018), it can also perform calculations efficiently inside them Stroev and Berloff (2023). Mapping mathematical operations onto photonic hardware enables the implementation of accelerators for neuromorphic computing Shastri et al (2021), analog optical processors Giamougiannis et al (2023); Wu et al (2022), and quantum integrated processors Harris et al (2017); Corrielli et al (2021).…”