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Quantum-Secure Cloud Deep Learning in Optical Networks

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

Published Jan 1, 2025• 0 citations
Publisher

Abstract

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.
DOI
10.1364/cleo_fs.2025.ff117_3

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The integration of quantum technologies into cloud computing represents a transformative advancement in information processing and storage capabilities. Quantum networking has emerged as a revolutionary paradigm that fundamentally alters how data is transmitted and secured, leveraging quantum mechanical principles to create theoretically unbreakable encryption systems. The development of quantum storage solutions has introduced unprecedented possibilities for information density and security through quantum mechanical systems utilizing atoms, ions, and photons. These technologies demonstrate remarkable potential in maintaining quantum coherence and implementing error correction protocols. While technical challenges persist in areas such as quantum decoherence, scalability, and infrastructure requirements, the applications of integrated quantum technologies span across multiple sectors, from ultra-secure cloud services to high-density data centers. The convergence of quantum computing with classical infrastructure has enabled hybrid computing approaches that maximize the advantages of both paradigms, particularly in fields such as molecular simulation, financial modeling, and cryptographic applications.