There has been a resurgence of interest in optical computing since the early 2010s, both in
academia and in industry, with much of the excitement centred around special-purpose …

Integrated photonic neural networks provide a promising platform for energy-efficient, high-
throughput machine learning with extensive scientific and commercial applications. Photonic …

Using photons in lieu of electrons to process information has been an exciting technological
prospect for decades. Optical computing is gaining renewed enthusiasm, owing to the …

As neural networks grow larger and more complex and data-hungry, training costs are
skyrocketing. Especially when lifelong learning is necessary, such as in recommender …

New developments in hardware-based 'accelerators' range from electronic tensor cores and
memristor-based arrays to photonic implementations. The goal of these approaches is to …

Probabilistic machine learning utilizes controllable sources of randomness to encode
uncertainty and enable statistical modeling. Harnessing the pure randomness of quantum …

This book is devoted to a comprehensive compilation of the first hardware platforms
employed for photonic reservoir computing. Reservoir computing is a machine learning …

Recently, integrated optics has gained interest as a hardware platform for implementing
machine learning algorithms. Of particular interest are artificial neural networks, since matrix …

Photonic neural networks | Nature Physics Skip to main content Thank you for visiting nature.com.
You are using a browser version with limited support for CSS. To obtain the best experience …

Delay-coupled electro-optical systems have received much attention for their dynamical
properties and their potential use in signal processing. In particular, it has recently been …