Bridging Theoretical Modelling, AI, and Quantum Concepts in Nanophotonics

Nanophotonics has become one of the most dynamic frontiers in modern science, enabling unprecedented control of light–matter interactions at the nanoscale. Recent advances in computational approaches, artificial intelligence (AI), and machine learning (ML) have accelerated the design and optimization of complex nanophotonic systems. These approaches allow researchers to explore large parameter spaces, perform inverse design of devices, and bridge theoretical models with experimental implementation.

At the same time, theoretical nanophotonics and plasmonics continue to provide fundamental insights into near-field phenomena, exciton dynamics, and radiative processes. Emerging areas such as quantum plasmonics and strong light–matter coupling extend the field toward single-photon control and quantum-enabled technologies. Mesoscopic optics, nonlocal effects, and dispersion phenomena in subwavelength media further enrich our understanding of light–matter interactions, pushing the boundaries of applicability for classical and quantum-optical concepts.

These parallel developments highlight the urgent need for an integrated perspective that bridges theory, computation, AI, and quantum concepts in nanophotonics. This Research Topic aims to provide support for the development of such a platform.

This Research Topic welcomes contributions from theory, computation, experiment, and interdisciplinary approaches that address the integration of computational, intelligent, and quantum perspectives in nanophotonics. Specific areas of interest include, but are not limited to:

• Computational nanophotonics: advanced numerical methods, multi-scale simulations, GPU/TPU-accelerated frameworks, and inverse design strategies.
• AI and ML for nanophotonics: deep learning, generative models, and reinforcement learning applied to photonic structure design, spectral prediction, and experimental data analysis.
• Optical neural computing: neuromorphic photonic architectures, all-optical accelerators, and parallel information processing platforms.
• Theoretical nanophotonics and plasmonics: near-field enhancement, nanoantennas, exciton dynamics, radiative heat transfer, and strong coupling phenomena.
• Quantum nanophotonics: quantum emitter–plasmon/cavity interactions, single-photon devices, and quantum state engineering.
• Mesoscopic and emerging topics: wave scattering, nonlocal and dispersion effects, topological photonics, 2D materials, nonlinear nanophotonics, and hybrid quantum–photonic systems.

We encourage contributions that highlight innovative methodologies, integrate AI with theory and experiment, or explore applications in quantum technologies, imaging, sensing, and photonic information processing.