analiza aiqbench

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.