Presentation Information
[10p-E219-9]Reinforcement Learning for Adaptive Intensity Control in BB84 QKD
〇(D)MAHIMA KUMAR1 (1.NETAJI SUBHAS UNIVERSITY OF TECHNOLOGY)
Keywords:
Quantum key distribution,Reinforcement learning,BB84 protocol
Quantum key distribution (QKD) guarantees cryptographic security through quantum mechanics, but practical BB84 systems fix the mean photon number μ at design time, wasting power on good channels and risking security on poor ones. We propose an adaptive framework where a Proximal Policy Optimization (PPO) reinforcement learning agent adjusts μ in real time based on the observed channel state. The channel is modelled as a three-state Markov chain representing Good (η=0.60), Medium (η=0.20), and Poor (η=0.06) transmittance conditions. The reward function jointly optimizes the asymptotic GLLP secure key rate and optical power consumption. Simulation over 200 evaluation episodes shows the PPO agent achieves a 0.90% improvement in secure key rate over a tuned static baseline, while maintaining QBER well below the 11% security threshold with zero violations. Results confirm that adaptive intensity control via reinforcement learning is a viable strategy for power-efficient, secure QKD systems.