Development and Justification of a Physical Layer Model Based on Monitoring Data for Quantum Key Distribution

TL;DR

This paper develops a machine learning-based physical layer model for Quantum Key Distribution systems, improving performance prediction accuracy and addressing practical system imperfections for enhanced long-term security.

Contribution

It introduces a novel ML approach to predict QKD system performance, surpassing traditional theoretical models in accuracy and practical applicability.

Findings

ML models outperform theoretical models in accuracy

Theoretical models provide foundational insights but are less practical

ML models adapt better to environmental and operational changes

Abstract

Quantum Key Distribution (QKD) is a promising technique for ensuring long-term security in communication systems. Unlike conventional key exchange methods like RSA, which quantum computers could theoretically break [1], QKD offers enhanced security based on quantum mechanics [2]. Despite its maturity and commercial availability, QKD devices often have undisclosed implementations and are tamper-protected. This thesis addresses the practical imperfections of QKD systems, such as low and fluctuating Secret Key Rates (SKR) and unstable performance. By applying theoretical SKR derivations to measurement data from a QKD system in Poland, we gain insights into current system performance and develop machine learning (ML) models to predict system behavior. Our methodologies include creating a theoretical QKD model [2] and implementing ML models using tools like Keras (TensorFlow [3]). Key findings reveal that while theoretical models offer foundational insights, ML models provide superior accuracy in forecasting QKD system performance, adapting to environmental and operational parameters. This thesis highlights the limitations of theoretical models and underscores the practical relevance of ML models for QKD systems. Future research should focus on developing a comprehensive physical layer model capable of doing long-term forcasting of the SKR. Such a model could prevent an encryption system form running out of keys if the SKR drops significantly. In summary, this thesis establishes a foundational approach for using ML models to predict QKD system performance, paving the way for future advancements in SKR long-term predictions.