Optical fibres process data at ultra-fast speeds

Imagine a computer that does not rely only on electronics but uses light to perform tasks faster and more efficiently. Two research teams from Finland and France have now demonstrated a novel way to process information using light and optical fibres, opening up the possibility to build ultra-fast computers.

The study, performed by postdoctoral researchers Dr Mathilde Hary from Tampere University and Dr Andrei Ermolaev from the Université Marie et Louis Pasteur, Besançon, demonstrated how laser light inside thin glass fibres 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 said.

Traditional electronics approaches its 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 fibres, 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 fibre 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 colours. By sending those into the fibre with a relative delay encoded according to an image, they show that the resulting spectrum of wavelengths at the output of the fibre 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 state-of-art digital methods, in under one picosecond.

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

“Performance is not simply a matter of pushing more power through the fibre. It depends on how precisely the light is initially structured; in other words, how information is encoded and how it interacts with the fibre properties,” Hary said.

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,” Ermolaev said.

Advancing optical nonlinearity through collaborative research in AI and photonics

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

Schematic of optical extreme learning machine using nonlinear fibre-optics propagation. Image credit: Mathilde Hary.

“This work demonstrates how fundamental research in nonlinear fibre 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,” said Professors Goëry Genty from Tampere University, John Dudley and Daniel Brunner from the Université Marie et Louis Pasteur, who led the teams.

The research combines nonlinear fibre optics and applied AI to explore new types of computing. In the future the 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.

Top image credit: iStock.com/Thomas-Soellner