Quantum Atomic Optical Neural Network

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 atomic optical neural network (QAONN) that utilizes atom-cavity neurons with controllable photon absorption and emission. These quantum optical neurons are designed to replace the electronic components in ONNs, such as photon detectors and emitters, which typically introduce delays and substantial energy consumption during nonlinear activation. To evaluate the performance of the QAONN, 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 QAONN to facilitate a real-world satellite image classification (SAT-6) task. Due to its compact hardware and low power consumption, the QAONN offers a promising solution for real-time satellite sensing, reducing communication bandwidth with ground stations and thereby enhancing data security.