Optical Neural Networks from Coherent Transient Dynamics in Waveguide QED

TL;DR

This paper introduces an all-optical neural network architecture utilizing coherent transient quantum dynamics in waveguide QED, enabling high-speed, low-latency processing without electronic conversion.

Contribution

It presents a novel fully optical neural network design leveraging transient light-matter interactions for nonlinear activation and programmable weights, advancing optical neuromorphic computing.

Findings

Achieved high classification accuracy on MNIST and colored-object recognition tasks.

Eliminated the optoelectronic activation bottleneck, reducing latency.

Demonstrated the feasibility of using transient light-matter dynamics for high-dimensional nonlinear processing.

Abstract

Optical neural networks promise ultrafast, low-energy information processing by performing computation directly with photons. Current implementations, however, are largely restricted to steady-state operation and rely on high-latency electro-optical conversion for nonlinear activation. To address these limitations, we propose an all-optical fully connected neural network architecture in which the basic neuronal functions are realized by coherent transient quantum dynamics. Within this framework, phase-tunable nonlocal interference in a giant cavity implements programmable synaptic weights; an integrator operating in the bad cavity regime performs temporal summation by coherently combining sequential wavepackets; and transient Rabi dynamics of a driven two-level system provide nonlinear activation. Full-physics simulations demonstrate high classification accuracy on MNIST and colored-object recognition tasks. These results eliminate the optoelectronic activation bottleneck, reduce latency, and establish transient light-matter dynamics as a native physical resource for high-dimensional nonlinear information processing, paving the way toward fully optical neuromorphic computing.