Uncovering the mechanisms of physics is driving a new paradigm in artificial intelligence
(AI) discovery. Today, physics has enabled us to understand the AI paradigm in a wide …

Quantum optical networks are instrumental in addressing the fundamental questions and
enable applications ranging from communication to computation and, more recently …

Recently emerging complex photonic structures exhibiting giant optical nonlinearity through
strong light-matter coupling require new theoretical approaches to accurately capture the …

This chapter describes the special artificial intelligence technique for increasing efficiency of
fuzzy information processing. The approach proposed by the authors consists of …

Compared to traditional neural networks, optical neural networks demonstrate significant
advantages in terms of information processing speed, energy efficiency, anti-interference …

A Sb_2S_3-based reconfigurable diffractive optical neural network (RDONN) for on-chip
integration is proposed. The RDONN can be integrated into standard silicon-on-insulator …

A remarkable surge in utilizing large deep-learning models yields state-of-the-art results in a
variety of tasks. Recent model sizes often exceed billions of parameters, underscoring the …

Limitations of electronics have stimulated the search for novel unconventional computing
platforms that enable energy-efficient and ultra-fast information processing. Among various …

The interplay between two paradigms, artificial intelligence (AI) and optical metasurfaces,
nowadays appears obvious and unavoidable. AI is permeating literally all facets of human …

Optical neural networks (ONNs) exhibit significant potential for accelerating artificial
intelligence task processing due to their low latency, high bandwidth, and parallel …