In the rapidly evolving world of artificial intelligence, the insatiable demand for larger and more complex models has spotlighted the urgent need for novel computing paradigms. Traditional electronic computers, although continually improving, face inherent limitations in power efficiency and processing capability that threaten to bottleneck future AI advancements. Researchers worldwide are now turning toward revolutionary technologies that harness the fundamental laws of physics themselves to process information more efficiently and at unprecedented speeds. One such emerging frontier is the development of physical neural networks, which employ analogue circuits rooted in photonics and quantum phenomena to mimic the behavior of artificial neural networks directly through physical interactions of light on silicon chips.
A groundbreaking study recently published in the prestigious journal Nature has unveiled significant progress in this field, showcasing collaborative efforts from leading global institutions including Politecnico di Milano, École Polytechnique Fédérale de Lausanne, Stanford University, University of Cambridge, and the Max Planck Institute. This research delves into the core challenge of training physical neural networks—an area that until now had largely been conceptual, hindered by the difficulty of adapting learning algorithms to operate within analogue physical substrates rather than traditional digital simulations.
At the heart of this innovation is the photonic microchip developed by the research team at Politecnico di Milano, which stands as a testament to the power of integrated photonic technologies. Unlike conventional electronic chips that rely on voltage and current states to represent and manipulate data, these photonic chips utilize the interference patterns of light waves to perform fundamental mathematical operations such as summations and multiplications. Remarkably, these operations occur on silicon microchips mere square millimeters in size, highlighting the extraordinary miniaturization and integration capabilities that photonics enables in hardware design.
This approach fundamentally transforms how data is processed. By bypassing the digital conversion steps that are traditionally necessary in electronic neural networks, photonic chips execute computational tasks natively in the analogue domain. According to Francesco Morichetti, the head of the Photonic Devices Lab at Politecnico di Milano, this leads to a profound reduction in both energy consumption and processing time. Such an advancement is pivotal, considering that modern AI data centers are notorious for their massive energy footprints. The capacity to perform AI computations in a more sustainable and efficient manner could redefine the environmental and economic landscape of machine learning.
Crucially, the study addresses one of the most vital aspects of neural network functionality: training. Training involves iteratively adjusting the network’s parameters until it successfully performs specific tasks or recognizes patterns in data. Traditionally, this process is computationally intensive and carried out within digital or software-based frameworks before deploying trained models onto hardware. The collaborative research introduces an innovative “in-situ” training methodology for photonic neural networks, whereby learning is conducted entirely within the physical system using light signals, without recourse to digital simulations.
This photonics-based in-situ training method embodies a paradigm shift by treating the physical network as the training medium itself. It leverages the intrinsic physics of light interference to update network weights and parameters directly on the photonic chip. Morichetti explains that this eliminates layers of data conversion and transfer between physical and digital domains, resulting in accelerated training speeds with enhanced robustness and efficiency. The potential to execute training faster, with lower latency, unlocks new possibilities for adaptive AI systems that can learn and evolve in real-time.
The implications of these advancements stretch far beyond laboratory prototypes. Photonic chips with optical neural network architectures could enable the creation of more sophisticated AI models capable of handling complex computations at the edge—on devices such as autonomous vehicles, drones, and portable sensors—without relying on centralized cloud processing. This decentralization of AI computation means systems can process real-time data locally, reducing communication lag, enhancing privacy, and improving resilience against network disruptions.
Moreover, photonic neural networks embody a confluence of multiple cutting-edge scientific disciplines, spanning applied physics, nanophotonics, optical materials, and computer science. The ability to engineer nanophotonic structures that manipulate photons with extreme precision enables the design of highly customizable neural architectures adapted for specific application domains. As research advances, this technology could birth new classes of AI hardware that operate fundamentally differently from silicon-based electronic processors, potentially surpassing them in speed, energy efficiency, and scalability.
One of the technical marvels enabling these developments is the exploitation of light interference—a phenomenon where multiple light waves overlap, amplifying or diminishing each other to yield precise mathematical outcomes on-chip. These analogue computations, inherently parallel and swift, contrast starkly with sequential electronic logic operations in conventional processors, offering significant advantages in throughput and power efficiency.
The collaborative nature of this research is also notable, bringing together experts from diverse institutions across Europe and the United States, each contributing unique expertise in photonics, AI, and applied sciences. Such interdisciplinary efforts underscore the complexity of transitioning physical neural networks from theoretical constructs to viable practical technologies. The paper, titled “Training of Physical Neural Networks,” provides a detailed commentary on the state of research in this domain and charts future pathways for overcoming remaining challenges.
In conclusion, the advent of physical neural networks powered by integrated photonic chips heralds a transformative step toward the next generation of intelligent machines. By leveraging the laws of physics to perform computational operations natively and efficiently, these systems promise to transcend the limitations of traditional digital computing architectures. Beyond merely accelerating AI workloads, they lay the groundwork for sustainable, adaptive, and decentralized intelligence embedded directly within everyday devices. The prospect of AI computations unfolding at the speed of light within miniature silicon photonic circuits marks a thrilling confluence of physics, engineering, and machine learning, poised to redefine how we conceive and deploy intelligent systems in the near future.
Subject of Research: Not applicable
Article Title: Training of physical neural networks
News Publication Date: 3-Sep-2025
Web References: DOI: 10.1038/s41586-025-09384-2
Image Credits: Politecnico di Milano, DEIB – Department of Electronics, Information and Bioengineering
Keywords
Artificial neural networks, Applied sciences and engineering, Physical sciences, Physics, Optical materials, Nanophotonics, Photonics, Applied optics, Neural net processing, Computer science, Computer processing, Generative AI, Machine learning
