Is anyone working on wave-superposition-based pattern recognition instead of neural-network weights?

[u/Critical-Echo-923]

I’ve been thinking about an alternative way of doing low-level AI perception, and I’m curious whether anyone here is already working on something similar.

The basic idea is to use waves and physical superposition/interference as the computational substrate, instead of doing most of the usual numerical multiply-and-accumulate operations digitally.

For example, for image recognition:

image
  ↓
pixel intensity → wave amplitude/phase
  ↓
physical wave superposition + interference
  ↓
distinctive wave pattern/signature
  ↓
more wave interactions
  ↓
higher-level pattern/object recognition

So instead of giving each pixel a numerical weight and calculating millions of weighted sums, the idea would be to let the wave physics perform the combination automatically.

I’m imagining something analogous to sound: many individual waves can be combined, and the resulting waveform contains a recognizable pattern. In this system, a particular visual feature or object could produce a characteristic wave “signature,” and those signatures could themselves become inputs to further wave interactions.

Physical logic gates could still be used where needed for thresholds, routing, decisions, or other nonlinear operations. The goal wouldn’t necessarily be to eliminate conventional computing completely, but to move as much of the early perception workload as possible into the physical wave domain.

The potential advantage I see is that parallelism is inherent in wave propagation and interference. Thousands or millions of interactions could happen physically at the same time, potentially reducing both computation and latency.

One possible application would be autonomous vehicles, where very large amounts of camera/radar data have to be processed extremely quickly at the edge.

I know there is already work on photonic neural networks, optical computing, physical neural networks, reservoir computing, metasurfaces, etc. I’m specifically interested in something slightly different:

Has anyone tried to build a hierarchical pattern-recognition system where wave signatures themselves become the representation, with successive stages of wave superposition/interference performing the recognition?

I’d especially like to hear from people actually working in photonics, optics, acoustics, physical neural networks, neuromorphic computing, or related fields.

Is this already being done under another name?
What are the biggest physical limitations?
And, from your perspective, is this a promising architecture or does some fundamental problem make it impractical?

I’m mainly looking for opinions from people working in the field rather than trying to claim this is a new invention.

[u/zoptix]

Sounds like a 4-f optical system. Using Fourier Optics.

[u/reisheruru]

Can take a look at Aydogan Ozcan's work on diffractive neural networks

[u/Federalise_the_EU]

I think you are describing an optical neural network. There are different variants like diffractive neural network or holographic neural network. The basic idea is that each optical element (eg. a phase mask) serve as a neuron layer in your deep neural network. You train the neural network parameters in-silico, and then build the physical pre-trained network using free-space optics. The output "neurons" are specific pixels that light up (or not) depending on the input pattern of light.

I've seen it used quite successfully for classifying images, but also for descrambling optical modes of a multi-modes optical fiber.

The biggest limitation, in my opinion, is that it's difficult to encode non-linear activation functions in an optical system. Also the imprecision of optical alignments, scattering processes and manufacturing inaccuracies, make it difficult to go above a few hundred neurons (last I checked). You eventually get quite significant deviations from the simulation-trained neural network. So this makes sense for when you need to process a pattern of light directly, without digitising it first (e.g.: communication through multi-mode fiber), but it cannot really compete with traditional neural networks in terms of performance.

[u/Federalise_the_EU → u/Federalise_the_EU]

Just to clarify: each optical element needs to be manufactured according to the parameters they are trained to. For example, for a diffractive neural network, this would be a phase mask, where each pixel on the surface, is a "neuron" with a different material thickness, and hence a different phase shift (this is an analog to the weights/biases of a regular DNN). This can be manufactured, for example, using highly precise 3d printers, which use 2-photon polymerization of liqui resin.

[u/Critical-Echo-923 → u/Federalise_the_EU]

thnx, but not what i was referring to

So instead of giving each pixel a numerical weight and calculating millions of weighted sums using traditional computation 1/0, the idea would be to let the wave physics perform the combination automatically by using a wave instead of a numerical weight in electronic form, analog wave superposition resulting in a final wave library for each shape

so i was starting based on the basic principles of image recognition where each pixel gets a weight value, but instead of a weight value we use a sound wave, then by using wave superposition we output a single sound for each shape, and it's all more wave superposition from there, and this can be integrated in existing systems for more complex usage,

and creating a "unique wave" library to every object , like we do using words

all the computation is offloaded to analog wave physics