Quantum optical neural networks using atom-cavity interactions to provide all-optical nonlinearity

TL;DR

This paper introduces a quantum optical neural network using atom-cavity interactions to achieve all-optical nonlinearity, aiming to improve processing speed and energy efficiency for machine learning tasks like image classification.

Contribution

The work presents a novel quantum optical neural network architecture based on atom-cavity systems, replacing electronic nonlinear components with quantum neurons for faster, low-power processing.

Findings

QONN achieves high accuracy on MNIST digit classification.

Convolutional QONN effectively classifies satellite images.

QONN demonstrates low power consumption and compact hardware design.

Abstract

Optical neural networks (ONNs) have been developed to enhance processing speed and energy efficiency in machine learning by leveraging optical devices for nonlinear activation and establishing connections among neurons. In this work, we propose a quantum optical neural network (QONN) that utilizes atom-cavity neurons with controllable photon absorption and emission. These quantum neurons are designed to replace the electronic components in ONNs, which typically introduce delays and substantial energy consumption during nonlinear activation. To evaluate the performance of the QONN, we apply it to the MNIST digit classification task, considering the effects of photon absorption duration, random atom-cavity detuning, and stochastic photon loss. Additionally, we introduce a convolutional QONN to facilitate a real-world satellite image classification (SAT-6) task. Due to its compact hardware and low power consumption, the QONN offers a promising solution for real-time satellite sensing, reducing communication bandwidth with ground stations and thereby enhancing data security.