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This research establishes a specific noise threshold of approximately 0.11 for the BB84 protocol, defining the boundary where quantum data paths remain secure against exfiltration while still enabling machine learning models to function within finite sample budgets. By linking classical PAC learning theory with observable quantum disturbance, the study provides a testable security condition that transforms abstract noise tolerance into a physically verifiable constraint for secure learning systems.

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A 0.11 threshold defines secure quantum data for learning

Quantum ZeitgeistSeptember 27, 20262 min read

This research defines a threshold—η_(BB84)≃0.11—for admissible quantum data paths, establishing a budget for secure learning, even with data exfiltration risks.

Jeongho Bang of Yonsei University established a security threshold of η_(BB84)≃0.11 defining acceptable noise levels in a BB84 protocol while still allowing a machine learning model to learn data securely and within a…

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