Innovative Enhancements in Quantum Key Distribution Using Multi-Decoy States, Advanced Bit Recovery, and Adaptive Filtering with ML

Abstract Quantum Key Distribution (QKD) stands as a ground breaking approach to secure communication, exploiting the principles of quantum mechanics to achieve theoretically unbreakable encryption. Despite substantial progress, challenges in optimizing key rates and ensuring robustness, particularly in high-loss environments, persist. This research proposes an advanced QKD method that integrates multiple decoy states with optimized intensity levels and pulse probabilities, advanced bit recovery techniques, and adaptive machine learning (ML) filtering to enhance signal-to-noise ratio (SNR). The proposed framework leverages ML-driven adaptive filtering to dynamically reduce noise, thereby improving the accuracy of gain and error rate calculations for decoy states and enhancing the efficiency of bit recovery. The bit recovery process incorporates permutation-based enhancement techniques, optimizing the recovery of unused quantum bits (RUQB). The proposed method is thoroughly evaluated through comparative analysis against leading QKD protocols, including E91, BBM92, B92, Six-state, Decoy State, and DSP protocols. The results demonstrate significant improvements in secure key rates, the percentage of correctly recovered bits, and overall system robustness, especially in challenging conditions. This research contributes to the advancement of QKD by providing a more resilient and efficient approach, addressing key limitations of existing methods, and paving the way for enhanced secure quantum communication systems.