Investigation of a Bit-Sequence Reconciliation Protocol Based on Neural TPM Networks in Secure Quantum Communications

TL;DR

This paper explores a neural network-based key reconciliation protocol for quantum key distribution, analyzing how synchronization efficiency and information leakage depend on quantum bit error rate and neural network parameters.

Contribution

It introduces a novel protocol transforming key material into neural network weights and studies its performance under various conditions.

Findings

Synchronization iterations increase with QBER

Leaked information decreases as weight range expands

Protocol shows potential for neural cryptographic key reconciliation

Abstract

The article discusses a key reconciliation protocol for quantum key distribution (QKD) systems based on Tree Parity Machines (TPM). The idea of transforming key material into neural network weights is presented. Two experiments were conducted to study how the number of synchronization iterations and the amount of leaked information depend on the quantum bit error rate (QBER) and the range of neural network weights. The results show a direct relationship between the average number of synchronization iterations and QBER, an increase in iterations when the weight range is expanded, and a reduction in leaked information as the weight range increases. Based on these results, conclusions are drawn regarding the applicability of the protocol and the prospects for further research on neural cryptographic methods in the context of key reconciliation.