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An optical neural network (ONN) is a neural network that uses light and optical hardware to carry out some of the weighted calculations in its layers. The light does the arithmetic; the network still learns and is described in the same terms as any other neural network. Most ONNs that exist today are experimental or specialized, and many depend on electronics for part of the work.
What an optical neural network is
A standard artificial neural network represents relationships through weighted connections. Each neuron combines its inputs using those weights, applies a nonlinear activation function, and passes the result to the next layer. An optical neural network keeps that structure but moves some of the weighted operations into optical hardware. “Optical” describes how the computation is carried out, not a different kind of learning objective or a different model of intelligence.
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The distinction matters because the phrase is often used loosely. Two things are easy to confuse:
- An optical neural network uses optics to perform computation. Light paths, interference, or diffraction implement some of the network’s weighted sums.
- A neural network sent over fiber is an ordinary electronic model whose inputs or outputs travel as light through fiber-optic links. The data moves optically, but the math is still done by electronic processors.
Only the first is an optical neural network in the technical sense.
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How an optical neural network works
The central operation in most neural layers is a matrix transformation, which is a large set of weighted sums. Optical systems can perform matrix multiplication by manipulating an optical field as it propagates. The field’s amplitude, phase, polarization, or angular momentum can carry values, and the optical elements transform those values as the light passes through them. Because the light travels through many paths at once, the operation can be parallel, which is the main reason the approach attracts interest.
In a trained network, the optical weights correspond to learned parameters. How those parameters are set depends on the architecture. Some designs can be reprogrammed; others use fixed connections between layers, with the trained structure built into the hardware itself.
Common optical building blocks
The 2024 review of optical neural network architectures lists the following implementations. The integration column reflects how the review classifies each family; where it does not place an example, the table says so.
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| Building block | What it does in an ONN | Architecture class in the 2024 review |
|---|---|---|
| Mach–Zehnder interferometer meshes | Interfere light across programmable paths to implement a matrix multiplication | Integrated (on-chip) |
| Microring-resonator weight banks | Use resonant rings to set the strength of individual wavelength channels | Integrated (on-chip) |
| 4f optical systems | Use lenses and a Fourier-plane arrangement to process an optical image | Non-integrated (free-space) |
| Diffractive optical elements | Shape light through patterned surfaces; connections between layers may be fixed | Non-integrated (free-space) |
| Wavelength-division multiplexing | Carries several signals on different wavelengths through the same path | Not stated in the review’s classification |
| Attenuator arrays | Scale optical signal strength to represent weight values | Not stated in the review’s classification |
Architecture families and how to compare them
The 2024 review divides ONNs into two broad groups. Non-integrated systems are built from volume optical components on a bench, while integrated systems place optical components on a chip. Free-space designs are generally easier to build with existing optics but need careful alignment. On-chip designs are more compact and easier to package, but they face their own limits.
There is no single “fastest” architecture. Compare them on these axes instead:
- Integration and footprint: on-chip designs can increase computational density and portability. Free-space systems use discrete elements that take up more space and need alignment.
- Reconfigurability: programmable components can adjust weights during use. Some diffractive designs use fixed connections or trained structures, which limits how easily they can be reused for other tasks.
- Nonlinearity: neural networks need a nonlinear activation at each layer. Implementing that step efficiently in optical hardware remains an open design challenge.
- Scale and input size: waveguide count, thermal effects, optical losses, and the conversion interfaces between light and electricity all constrain how large a network can become.
- End-to-end system cost: lasers, modulators, detectors, electronic control, and data movement count toward the total. A fast optical operation can still produce a slow system if the surrounding electronics are the bottleneck.
All-optical versus hybrid systems
An all-optical network would perform every layer’s computation in light. In practice, the 2024 review states that ONNs generally could not complete inference independently of electronic computing hardware. Electronics commonly handle parameter reconstruction, nonlinear operations, data storage, and flow control. Most deployed or demonstrated systems are therefore hybrid optoelectronic systems, where the optical part accelerates specific operations and electronics handle the rest.
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When you read a claim about an ONN, check which description applies. A result for the optical stage alone is not the same as a result for the complete system.
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The 2024 review describes low latency, high bandwidth, low power consumption, and parallel signal processing as potential advantages of optical computing. These are potential benefits, not guaranteed ones. Their size depends on the workload, the hardware, and what is counted in the measurement.
The same review identifies challenges around computational density, nonlinearity, scalability, reconfigurability, and practical application. It also notes that ONNs were not as widely applied as their electronic counterparts, and that practical application scenarios remained rare in its 2024 account. A 2025 commentary describes progress toward more complete optical pipelines but states that these systems remain far from electronic accelerators in scale or configurability.
The motivation has been stated for decades. In a 1987 Optica abstract titled “Optical Neural Computers,” Demetri Psaltis wrote: “With optics it is feasible to realize the dense connectivity that is evident in neural networks.” That capability is the reason the idea persists, but feasibility of connectivity is not the same as a complete, practical system.
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The 2024 review discusses several demonstrations: optical processing for imaging and sensing, nonlinear compensation in submarine fiber-optic communication links, and photonic deep-learning inference on edge devices. These are specific demonstrations. They should not be read as evidence of broad commercial deployment.
The review also names Lightmatter, along with its Envise and Passage products, as examples of industry activity in optical computing. Those names reflect the review’s 2024 publication date. Product status changes quickly, so check the vendor directly before assuming any of these is currently available.
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Is optical computing replacing GPUs?
Not on the evidence available. Optical neural networks are an active area of research and early commercial development, but the 2024 review and the 2025 commentary both describe them as specialized and far from electronic accelerators in scale and configurability. Any claim that optical hardware will broadly replace GPUs goes beyond what current evidence supports. The more accurate picture is that optics may accelerate particular operations, often alongside electronics, in particular settings.
In short, an optical neural network uses light to perform part of a neural network’s weighted computation, and the benefits reported so far are conditional on workload and system design.
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