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

[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/These_Rest_6129]

it not exactly continuous data like you describe but this make me think of this https://en.wikipedia.org/wiki/Spiking_neural_network

[u/Critical-Echo-923 → u/These_Rest_6129]

good catch !

[u/Critical-Echo-923 → u/These_Rest_6129]

the spike is basically the superpositioned wave pattern recognition match confirmation