Machine Learning Enhances Quantum Key Distribution Performance
A March 7, 2026 arXiv preprint surveys machine learning techniques applied to improve Quantum Key Distribution (QKD) security and performance. It reviews five application areas—parameter optimization, attack detection, protocol selection, performance prediction, and network management—reporting improvements in reduced QBER and increased Secret Key Rate (SKR). The authors recommend lightweight, generalizable models and standardized benchmarks to enable scalable, real-world ML-enhanced QKD deployments.
Scoring Rationale
Comprehensive survey with practical ML recommendations; limited novelty and single arXiv preprint reduce immediate impact.
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