The QETRA Protocol: Pioneering Quantum Eavesdropping Detection with Classical SVM

Quantum cryptography, specifically Quantum Key Distribution (QKD), currently operates under the paradigm of detecting eavesdropping through quantum bit error rate (QBER) analysis, as exemplified by the BB84 protocol.

This preprint presents the QETRA protocol, which introduces a novel approach to eavesdropping detection within the existing QKD paradigm. Utilizing a classical Support Vector Machine (SVM) to analyze quantum circuit outcomes, QETRA enhances the existing methodology rather than proposing a radical shift. This places the research within the “Normal Science” stage. However, the explicit aim of improving upon and potentially surpassing existing QBER analysis methods suggests an underlying “Model Drift”. While operating within the accepted paradigm, QETRA pushes the boundaries by introducing a new tool (classical SVM) and aims to improve current limitations, hinting at a possible future shift if the method proves significantly superior. The observed anomalies, although requiring further investigation, also contribute to the potential drift, indicating that the current model may not fully encompass all observed phenomena. Therefore, due to the combination of working within the existing QKD paradigm and the introduction of classical machine learning as a complementary analysis technique, the most relevant classifications are Normal Science and Model Drift.