Quantum-Secure Cloud Deep Learning in Optical Networks

The demand for cloud-based deep learning has intensified the need for secure computation. Our quantum-secure deep learning protocol leverages quantum properties of light and telecommunication components for scalable, secure cloud-based deep learning in optical networks.

Authors

Dirk Englund (ORCID: https://orcid.org/0000-0002-1043-3489)
Sri Krishna Vadlamani (ORCID: https://orcid.org/0000-0003-4602-0771)
Ryan Hamerly (ORCID: https://orcid.org/0000-0003-4976-2236)
Kfir Sulimany
Prahlad Iyengar

Publication Details

Published: 2025-01-01
DOI: https://doi.org/10.1364/cleo_fs.2025.ff117_3
Primary Topic: Neural Networks and Reservoir Computing
Type: article
Field-Weighted Citation Impact: 0.00

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article

Quantum-Secure Cloud Deep Learning in Optical Networks

Dirk Englund, Sri Krishna Vadlamani, Ryan Hamerly, Kfir Sulimany et al.

Jan 1, 2025

Neural Networks and Reservoir Computing

article

Quantum-Secure Cloud Deep Learning in Optical Networks

Dirk Englund, Sri Krishna Vadlamani, Ryan Hamerly, Kfir Sulimany, Prahlad Iyengar

January 1, 2025article en

Abstract

Openalex Percentile: Top 87%

Neural Networks and Reservoir Computing

DOI Source OpenAlex

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Quantum-Secure Cloud Deep Learning in Optical Networks — Dirk Englund, Sri Krishna Vadlamani, et al. · (2025) | TGRS Research Map | TGRS