麻豆淫院

Imagine a computer that does not rely only on electronics but uses light to perform tasks faster and more efficiently. A collaboration between two research teams from Tampere University in Finland and Universit茅 Marie et Louis Pasteur in France have now demonstrated a novel way of processing information using light and optical fibers, opening up the possibility of building ultra-fast computers. The studies are published in and on the preprint server.

The research was performed by postdoctoral researchers Dr. Mathilde Hary from Tampere University and Dr. Andrei Ermolaev from the Universit茅 Marie et Louis Pasteur, Besan莽on, demonstrated how laser light inside thin glass fibers can mimic the way artificial intelligence (AI) processes information. Their work has investigated a particular class of computing architecture known as an Extreme Learning Machine, an approach inspired by neural networks.

"Instead of using conventional electronics and algorithms, computation is achieved by taking advantage of the nonlinear interaction between intense light pulses and the glass," Hary and Ermolaev explain.

Traditional electronics approach their limits in terms of bandwidth, data throughput and power consumption. AI models are growing larger, they are more energy-hungry, and electronics can process data only up to a certain speed. Optical fibers, on the other hand, can transform input signals at speeds thousands of times faster and amplify tiny differences via extreme nonlinear interactions to make them discernible.

Towards efficient computing

In their recent work, the researchers used femtosecond laser pulses (a billion times shorter than a camera flash) and an optical fiber confining light in an area smaller than a fraction of human hair to demonstrate the working principle of an optical ELM system. The pulses are short enough to contain a large number of different wavelengths or colors.

By sending those into the fiber with a relative delay encoded according to an image, they show that the resulting spectrum of wavelengths at the output of the fiber transformed by the nonlinear interaction of light and glass contains sufficient information to classify handwritten digits (like those used in the popular MNIST AI benchmark). According to the researchers, the best systems reached an accuracy of over 91%, close to the state-of-the-art digital methods, in under one picosecond.

What is remarkable is that the best results did not occur at the maximum level of nonlinear interaction or complexity, but rather from a delicate balance between fiber length, dispersion (the propagation speed difference between different wavelengths) and power levels.

"Performance is not simply matter of pushing more power through the fiber. It depends on how precisely the light is initially structured, in other words how information is encoded, and how it interacts with the fiber properties," says Hary.

By harnessing the potential of light, this research could pave the way towards new ways of computing while exploring routes towards more efficient architectures.

"Our models show how dispersion, nonlinearity and even quantum noise influence performance, providing critical knowledge for designing the next generation of hybrid optical-electronic AI systems," continues Ermolaev.

Advancing optical nonlinearity through collaborative research in AI and photonics

Both research teams are internationally recognized for their expertise in nonlinear light鈥搈atter interactions. Their collaboration brings together theoretical understanding and state-of-the-art experimental capabilities to harness optical nonlinearity for various applications.

"This work demonstrates how fundamental research in nonlinear fiber optics can drive new approaches to computation. By merging physics and machine learning, we are opening new paths toward ultrafast and energy-efficient AI hardware," say Professors Go毛ry Genty from Tampere University and John Dudley and Daniel Brunner from the Universit茅 Marie et Louis Pasteur, who led the teams.

The research combines nonlinear fiber optics and applied AI to explore new types of computing. In the future, their aim would be to build on-chip optical systems that can operate in real time and outside the lab. Potential applications range from real-time signal processing to environmental monitoring and high-speed AI inference.