Self-optimizing multichannel optical computing

TL;DR

This paper presents a self-optimizing multichannel optical computing system that processes RGB images and numerical data efficiently, using novel autonomous optimization strategies to significantly improve accuracy in various tasks.

Contribution

It introduces a multichannel optical computing architecture with autonomous optimization methods, enabling practical and high-performance optical machine learning applications.

Findings

Improved accuracy by 26-58 percentage points over raw pixel baselines.

RGB processing outperforms grayscale by 5-6 percentage points.

Self-optimization adds an extra 6-7 percentage points in accuracy.

Abstract

Optical computing offers ultrafast, energy-efficient alternatives to conventional digital processors, yet most implementations remain confined to single-channel processing, severely underutilizing light's information capacity. Here we demonstrate a self-optimizing multichannel optical computing architecture based on multi-plane light conversion that natively processes RGB images and structured numerical data throughout the optical domain. We introduce two complementary optimization strategies that enable autonomous performance adaptation without differentiable forward models. First, Bayesian optimization tunes channel mixing coefficients to minimize crosstalk and enhance feature separability at the input level. Second, a hardware-in-the-loop protocol based on self-organized criticality leverages avalanche dynamics to autonomously navigate the high-dimensional phase landscape, enabling the system to self-optimize through stochastic multi-scale perturbations. Across medical imaging, natural image classification, and regression tasks, multichannel processing with random phase masks improves accuracy by 26--58 percentage points over raw pixel baselines, with RGB systematically outperforming grayscale by 5--6 percentage points. Self-optimization strategies provide additional gains of 6--7 percentage points through autonomous adaptation at complementary system levels. Our work establishes self-optimizing multichannel optical computing as a practical platform for real-world machine learning applications.