# Photonic AI: A Hybrid Diffractive Holographic Neural System for Passive Optical Real-Time Image Classification

Source: [Photonic AI: A Hybrid Diffractive Holographic Neural System for Passive Optical Real-Time Image Classification](<https://www.sota2.com/research/paper/69e5b3027d0172140ec7df17>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-04-21T05:00:04.784Z

Authors: Prakul Sunil Hiremath

Published: 2026-04-14T10:34:14.000Z

## Abstract

Edge intelligence is constrained by the energy and latency costs of shuttling data through electronic memory hierarchies. Optical systems offer a fundamentally different computational regime: once an input wavefront is launched into a structured medium, propagation, diffraction, and interference jointly enact a linear transformation whose cost is determined by wave physics rather than by clocked arithmetic. This paper develops a rigorous systems-level treatment of that regime and introduces a hybrid diffractive holographic architecture for image classification. The proposed model couples a Diffractive Optical Neural Network (DONN) with a Holographic Interference-Based Learning (HIBL) operator a formal map from digitally optimized phase distributions to physically realizable, fabrication-compatible interference patterns embeddable in passive optical elements. We express the full inference pipeline as a composition of encoding, phase modulation, free-space propagation, and intensity measurement operators, making explicit which quantities are learned, which are fixed by design, and where nonlinearity enters through photodetection. This operator-theoretic view resolves a persistent gap in the optical-ML literature between learning a transformation and physically realizing it. In physics-informed simulation on MNIST, a three-layer system with approximately 25,000 phase elements achieves 91.2% test accuracy with propagation-limited nanosecond-scale latency. The primary contribution is not a performance claim but a precise computational framework: learned representations can be physically embedded into structured optical media so that inference is executed by wavefront transformation through a passive, fabricated object rather than by sequential electronic multiply accumulate operations.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2604.15364>)

[PDF](<https://arxiv.org/pdf/2604.15364.pdf>)