Optimization of CV-QKD Under Practical Constraints
arXiv:2605.0204537.3
AI Analysis
This work addresses the practical deployment of CV-QKD by optimizing for real-world hardware limitations, which is important for making quantum key distribution more feasible.
The paper uses reinforcement learning to optimize continuous-variable quantum key distribution (CV-QKD) under practical hardware constraints such as limited filter taps, mean photon number, and finite DAC/ADC resolution, achieving significant performance improvements.
Using reinforcement learning, we optimize for practical hardware constraints, including limited FIR filter taps at the transmitter and receiver, mean photon number and finite DAC/ADC resolution. Under these realistic conditions, the proposed approach achieves significant performance improvements.