Photonic computing has emerged as a powerful framework for next-generation information processing by exploiting the high bandwidth, low latency, and massive parallelism of light. Photonic platforms offer a compact and scalable substrate for a broad range of computational tasks. Over the past decade, this capability has advanced substantially through progress in low-loss device design, programmable photonic circuits, on-chip nonlinear effects, and dense heterogeneous integration. These developments have positioned photonic computing as a promising route for artificial intelligence and machine learning, while extending its relevance to signal processing applications in high-speed optical communications, sensing, data centers, cloud computing, and emerging quantum technologies. In parallel, growing interest has focused on neuromorphic photonics and hardware-accelerated approaches to complex optimization problems.
This Research Topic aims to capture recent progress and emerging directions in photonic computing, spanning fundamental concepts, devices, computing architectures, and system-level implementations. Central challenges in the field include energy-efficient linear acceleration, scalable nonlinear processing, cascadable operations, photonic memory, signal-conversion overhead, and device to system-level reconfigurability. At the same time, the field has diversified rapidly, spanning optical neural networks, neuromorphic and reservoir systems, analog photonic processors, and photonic machines for combinatorial and dynamical optimization. The Research Topic therefore seeks contributions that critically examine the computational role of photonics across these directions, identify the strengths and limitations of different approaches, and clarify the relationship between device physics, architecture, and application-level performance. The collection will help bridge the gap between proof-of-principle demonstrations and realistic photonic computing technologies.
We welcome original research articles, reviews, perspectives, and roadmap papers that provide new insights into the fundamentals and applications of photonic computing. Submissions may present theoretical studies, numerical modeling, or experimental demonstrations, and should advance understanding of current challenges and future opportunities in this rapidly evolving field. Topics of interest include, but are not limited to:
• optical neural networks; photonic neuromorphic and reservoir computing; • photonic spiking neural networks; • photonic hardware accelerators; • physics-inspired and analog photonic computing; • in-memory computing; • photonic approaches to combinatorial optimization, including Ising machines.
We also encourage contributions on enabling technologies, including integrated photonic circuits, nonlinear optical devices, programmable photonic architectures, photonic memory, optical interconnects, hybrid photonic-electronic systems, and novel training strategies.
Article types and fees
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Brief Research Report
Conceptual Analysis
Data Report
Editorial
FAIR² Data
General Commentary
Hypothesis and Theory
Methods
Mini Review
Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.
Article types
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Brief Research Report
Conceptual Analysis
Data Report
Editorial
FAIR² Data
General Commentary
Hypothesis and Theory
Methods
Mini Review
Opinion
Original Research
Perspective
Review
Keywords: optical neural networks, photonic neuromorphic and reservoir and spiking neural computing, photonic hardware accelerators, photonic processing of spectral and polarization and temporal information, training methods for photonic neural networks, photonic computing architectures, photonic convolutional accelerators, photonic machine learning, diffractive photonic processors, photonic Ising machines, photonic extreme learning machines, integrated photonic circuits, programmable photonic architectures, nonlinear optical devices, photonic memory
Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
