Photonic Neural Networks

A special issue of Photonics (ISSN 2304-6732).

Deadline for manuscript submissions: closed (15 February 2022) | Viewed by 3080

Special Issue Editor


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Guest Editor
Senior Research Scientist, Science Systems and Applications Inc., Lanham, MD, USA
Interests: optoelectronics; deep learning; solid state physics

Special Issue Information

Dear Colleagues,

Artificial neural networks (ANNs) are recognized for their enormous potential across many scientific disciplines and economic sectors. They have therefore rapidly evolved in the past decade. Although traditional computing architectures such as central processing units (CPUs) or graphic processing units (GPUs) were originally used by computer science experts to implement their ANN algorithms and ideas, growing expectations in the artificial intelligence (AI) industry demands faster and more power efficient computing solutions to run demanding deep learning tasks. The developments in ANNs coincide with tremendous developments in the field of photonic materials and integrated photonic circuits. Over the years, photonic computing studies and products have demonstrated a clear advantage in communication and processing speed over their electronic rivals due to the inherent parallelism of the bosonic nature of light. Furthermore, many linear and non-linear transformations were demonstrated using passive optical components, meta devices, and active photonic components. To further advance the field of photonic neural networks (PNN), we encourage you to submit your work to this Special Issue. Original research papers or review articles providing insights into the state-of-the-art and future of the field are welcome. This Special Issue focuses on:

(a) Photonic integrated circuit architectures for matrix multiplications,

(b) Photonic neuromorphic computing,

(c) Reservoir computing,

(d) Device components for implementing nonlinear functions in photonic neural networks, and

(e) Application opportunities for photonic neural networks.

Dr. Mohammad H. Tahersima
Guest Editor

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Published Papers (1 paper)

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19 pages, 1146 KiB  
Article
Multi-Attack Detection: General Defense Strategy Based on Neural Networks for CV-QKD
by Hongwei Du and Duan Huang
Photonics 2022, 9(3), 177; https://doi.org/10.3390/photonics9030177 - 12 Mar 2022
Cited by 7 | Viewed by 2268
Abstract
The security of the continuous-variable quantum key distribution (CVQKD) system is subject to various attacks by hackers. The traditional detection method of parameter estimation requires professionals to judge known attacks individually, so the general detection model emerges to improve the universality of detection; [...] Read more.
The security of the continuous-variable quantum key distribution (CVQKD) system is subject to various attacks by hackers. The traditional detection method of parameter estimation requires professionals to judge known attacks individually, so the general detection model emerges to improve the universality of detection; however, current universal detection methods only consider the independent existence of attacks but ignore the possible coexistence of multiple attacks in reality. Here, we propose two multi-attack neural network detection models to handle the coexistence of multiple attacks. The models adopt two methods in multi-label learning: binary relevance (BR) and label power (LP) to deal with the coexistence of multiple attacks and can identify attacks in real-time by autonomously learning the features of known attacks in a deep neural network. Further, we improve the model to detect unknown attacks simultaneously. The experimental results show that the proposed scheme can achieve high-precision detection for most known and unknown attacks without reducing the key rate and maximum transmission distance. Full article
(This article belongs to the Special Issue Photonic Neural Networks)
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