Photonics for AI and AI for Photonics: Recent Progress and Future Directions

The intersection of artificial intelligence and photonics is generating new discoveries across both fields. Artificial intelligence is changing how photonic systems are designed, modeled, and controlled, especially in problems where several parameters, nonlinear effects, and fabrication constraints make conventional design approaches slow or inefficient. Data-driven methods are now helping researchers solve inverse problems, optimize device layouts, improve system control, and predict performance with greater speed and accuracy. At the same time, photonic platforms are emerging as strong candidates for artificial neural networks because they offer high bandwidth, parallel signal processing, low latency, and reduced energy consumption compared with standard electronic architectures. This two-way interaction is opening new paths for photonic computing, optical communications, sensing, and real-time information processing, with growing relevance for both scientific research and practical applications.

This Research Topic aims to highlight recent progress in the integration of artificial intelligence and photonics, with a focus on classical photonic systems and applications. The goal is to bring together work that shows how artificial intelligence can improve the design, optimization, control, and robustness of photonic devices and systems, and how photonic hardware can support fast and energy-efficient machine learning and neuromorphic computing. Despite rapid progress, major challenges remain, including scalability, noise, fabrication tolerances, device variability, calibration drift, training-to-hardware mismatch, energy overhead, and the difficulty of achieving high throughput and low latency under realistic hardware constraints. This Research Topic targets contributions that address these challenges through theoretical analysis, numerical modeling, experimental demonstrations, and application-oriented studies. Its aim is to offer a broad and timely view of the field while supporting the development of practical photonic technologies for next-generation computation, communication, sensing, and related artificial intelligence applications.

We welcome original research articles, review papers, mini reviews, and perspective contributions aligned with the scope of this issue. Brief Research Reports and Opinion manuscripts will also be considered when they align with the scope of this collection and the journal’s Author Guidelines. Topics of interest include optical neural networks, neuromorphic photonics, photonic computing, machine learning for photonic device design, inverse design methods, optimization strategies, physics-aware training, robustness against noise and imperfections, and experimental demonstrations of AI-enabled photonic systems. Contributions on integrated photonics, nonlinear photonic platforms, optical signal processing, and hardware-efficient machine learning are also encouraged. Submissions should present clear advances of broad interest to the community, whether at the device, system, algorithmic, or application level. Work connecting theory with experimental validation or practical implementation is especially encouraged. Authors who are uncertain whether their work fits the scope of the Research Topic are encouraged to contact the Topic Editorial Team before submission.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• ... View all formats

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
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review

Keywords: Neuromorphic photonics, artificial Intelligence, neural netand Classical and Quantum Machine Learning, Optical Neural Networks, Mode-locked Lasers, Photonic Optimization

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.