Jena LIGHT – Photonics meets AI

Information about the conference "Jena LIGHT – Photonics meets AI"

Being Europe’s city of light, Jena has been pushing the frontiers of photonics for more than a century. Recently, the potential of photonic systems to create the fastest and most energy efficient artificial intelligence (AI) platforms has been recognized. Therefore, the experts of photonics and AI technology will gather to exchange their view on the future of this emerging field in research and applications in an interdisciplinary conference in Jena.

The conference "Jena LIGHT – Photonics meets AI" took place on 3-6 September 2024, hosted by the Abbe Center of Photonics at the Friedrich Schiller University Jena, Germany. The conference was organized jointly by the university’s LIGHT profile, the International Research Training Group Meta-Active (IRTG 2675),), the Collaborative Research Center NOA (CRC 1375), and the CZS Nexus group Metasurfaces for Diffractive Deep Neural Networks (MetaNN)External link.

Aim of the conference

To truly push the capability of future neuromorphic photonic systems for facilitating AI, there is an immediate need for developing neural network architectures and machine learning algorithms that can best utilize the underlying physics of the photonic systems, especially their high dimensionality. In this regard, there is an urgent need to bring together the expertise of computer scientists active in the field of AI and machine learning with the research communities of neuromorphic photonics and quantum information. 

The conference "Jena LIGHT – Photonics meets AI" particularly aims to bring together world-leading researchers in the field of neuromorphic photonic systems, quantum photonic technologies, nanooptics, and computer science, to explore and discuss the current challenges and future potentials in the field of neuromorphic photonics. With this perspective, the topics of interest include, but are not limited to:

• Diffractive and integrated photonic neural networks
• High-dimensional photonic information processing
• Machine-learning and other computational methods (also with the potential) for training and design of photonic neuromorphic systems
• Photonic quantum information processing platforms with potential application for neuromorphic photonic realizations
• Quantum machine learning and quantum neural networks
• Photonic reservoir computing

Within these topics, different aspects on the physics, fabrication, characterization, and programming of relevant photonic hardware platforms and also relevant computational and design methods are included in the scope of the symposium.