Artificial Intelligence
Training AI With Optical Fiber: A Light-Based Leap
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Why Optical Fiber Could Replace Electricity in AI Computing
Since the early days of computing, almost all computers have been based on calculations using electricity in one way or another, from antique vacuum tubes to modern nanometer-scale silicon chips.
As silicon chips get smaller and smaller, researchers have been looking at new ways to build computers that could push our capacity further than silicon chips, a topic we explored in “Top 10 Non-Silicon Computing Companies”.
These methods include using different materials, like carbon carbide, vanadium dioxide, organic materials, or graphene, for example. Another way is to change how computing is done, moving away from the binary programming of electricity-based computing, which includes quantum computing and photonics.
Photonics uses light instead of electricity to encode and transfer information. However, until now, it has still been ultimately converted into a binary signal, failing to form a purely light-based form of computation.
This has changed with the work of researchers at the Tampere University (Finland) and Université Marie et Louis Pasteur (Besançon, France). They used optical fiber for ultrafast calculations and published their findings in the scientific journal Optics Letters1, under the title “Limits of nonlinear and dispersive fiber propagation for an optical fiber-based extreme learning machine”.
Limitations of Traditional AI Training With Electronic Systems
AI training and data processing are reaching limits in terms of efficiency, with AI computation increasingly constrained by energy consumption and the speed of data processing.
In contrast, light-based calculations have the potential to be thousands of times quicker and can encode data into tiny differences of energy, making it more efficient. The issue is that so far, no direct calculation using light has been performed.
The researchers’ work used a particular class of computing architecture known as an Extreme Learning Machine, (ELM) an approach inspired by neural networks.
Among some of their advantages, ELMs can learn from the training data in one step and are a relatively simple algorithm.
As a rule, ELM is unlikely to be useful for very complex tasks requiring multiple layers of AI training, but can perform very well and more efficiently for specific tasks, like visual recognition for example.
How Researchers Encoded Images Using Optical Fibers
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 build an optical ELM system.
The laser pulses are short enough to contain a large number of different wavelengths or colors, creating a rich dataset.
They then sent these data into the fiber with a relative delay encoded according to an image.
Source: Tampere University
The Role of Nonlinear Optics in AI Processing
This form of data encoding was transformed by the nonlinear interaction of light and glass.
Linear optics is the regular optics taught in school, where the light directly interacts with a prism, for example.
In non-linear optics, the reaction of the medium in which the light passes depends on the light’s wavelength, intensity, direction, and polarization.
Nonlinear optical components can cause photons of different frequencies to combine and create new photons at new frequencies.
“Instead of using conventional electronics and algorithms, computation is achieved by taking advantage of the nonlinear interaction between intense light pulses and the glass.”
Mathilde Hary and Andrei Ermolaev – Post-Doctoral Researchers
Non-linear interaction and Extreme Learning Machine (ELM) algorithm was able to train an AI to classify handwritten digits (like those used in the popular MNIST AI benchmark).
Source: Optics Letters
The best systems reached an accuracy of over 91%, close to the state-of-the-art digital methods.
What makes the result exceptional is that it was achieved in under one picosecond, or one trillionth of a second (0.000000000001 seconds).
Ideal Optimization
The best results did not occur at the maximum level of nonlinear interaction or complexity.
Instead, they required 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.”
Are Optical Fiber Computers the Future of AI?
Training AIs with only light is a radical departure from all the methods used until now. This is likely not going to be a method possible to use for every type of data, but for the ones where it can be applied, this could bring results that are 1,000x more efficient in terms of energy, and as much as a million times quicker.
“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.”
Most likely, such an approach would mean that some AI calculation would be delegated to a non-linear optical fiber hardware custom-built for the task. So repetitive tasks, like visual identification, would be the best candidates more than processing new data.
“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.
Potential applications range from real-time signal processing to environmental monitoring and high-speed AI inference.
Such work is, however, still at the demonstration of basic principles of the technique stage, and far from a commercialization step.
It nevertheless demonstrates that photonics is likely going to be an increasingly important part of the computing industry moving forward, as light can be superior to electricity for some computing applications due to fundamental physics reasons.
Top Publicly Traded Laser & Photonics Company
Coherent (II-VI Marlow): A Leader in Laser Innovation
(COHR )
Coherent is a large industrial conglomerate with 26,000+ employees and a leader in laser technology. It resulted from the merger of advanced material II-VI Marlow with laser maker Coherent.
The company is an expert in advanced materials used in lasers, optics, and photonics, such as indium phosphide, epitaxial wafers, and gallium arsenide.
It grew largely thanks to multiple acquisitions over the last decade, from $600M in revenues in 2013 to $4.7B in 2024.
The company derives 29% of its revenues from lasers directly, with the rest linked to associated equipment like optical fiber, and electronics. The instrumentation category mostly includes life
sciences and medical applications.
Source: Coherent
The presence of the company in advanced materials like thermophotovoltaics (which we discussed in a previous article), silicon carbide, lasers, and electronics helps it benefit from structural trends like the growth of precision manufacturing, additive manufacturing (3D printing), electrification, and renewable energies.
The company has recently separated its silicon carbide business into a new entity, owned at 75% by Coherent, with the rest owned equally by its partners Mitsubishi Electric (bringing silicon carbide power IP) and Denso (bringing its activity as an automotive supplier on electrification and power semiconductors).
This is because silicon carbide is increasingly its own technology, mostly used in high-power applications like EVs, batteries, and renewable energy.
Coherent is a leader in LIDAR and 3D-digital sensing, including for self-driving applications, biotech Next Generation Sequencing (NGS) Flow Cells, and lasers for semiconductor manufacturing. It expects its main markets to grow at 8-20%.
Source: Coherent
The other potential new applications of lasers, like direct energy weapons, photonic computing, nuclear fusion, and spacetech, could all equally help sustain the long-term growth of the company.
Overall, Coherent is as close as it can get to a “pure play” publicly traded laser company for investors interested in the sector, with strong vertical integration and 3,100+ patents protecting its innovations.
As photonics progresses, it will progressively increase the demand for ultra-fast, ultra-precise laser systems, as well as lasers used in optical telecommunications.
Latest Coherent (COHR) Stock News and Developments
Study Referenced
1. Andrei V. Ermolaev, Mathilde Hary, et al. Limits of nonlinear and dispersive fiber propagation for an optical fiber-based extreme learning machine. Optics Letters. Vol. 50, Issue 13, pp. 4166-4169 (2025) https://doi.org/10.1364/OL.562186
