As Quantum Key Distribution (QKD) continues to evolve, one of the major challenges remains its integration into existing metropolitan optical networks. Two key obstacles have been identified:
1️⃣ Attenuation of quantum signals over fiber and through network components
2️⃣ Photon contamination caused by the coexistence of classical signals in the same fiber
To address these challenges, our team at ALLEGRO has developed and tested a Machine Learning-based diagnostic tool [EUL_ML1] designed to monitor and classify impairments on quantum channels in real time — without needing access to physical-layer parameters.
Using only QBER (Quantum Bit Error Rate) and SKR (Secret Key Rate) time-series data, our ML model identifies patterns that indicate critical, irreversible impairments — enabling smarter decisions and proactive measures to maintain quantum channel integrity.
The result? A scalable, technology-agnostic solution that strengthens the reliability and integration of QKD in classical communication networks. 💡🔐
Proud to be contributing to the future of secure quantum communications!
#QuantumSecurity #QKD #MachineLearning #ALLEGROProject #QuantumNetworking #Innovation #Photonics #TelecomSecurity #ML
