Optimization of CV-QKD Under Practical Constraints

TL;DR

This paper employs reinforcement learning to optimize continuous-variable quantum key distribution (CV-QKD) systems considering real-world hardware limitations, resulting in notable performance enhancements.

Contribution

It introduces a reinforcement learning-based optimization method tailored for practical CV-QKD hardware constraints, a novel approach in this context.

Findings

Achieves significant performance improvements under practical constraints.

Demonstrates effectiveness of reinforcement learning in quantum communication hardware optimization.

Abstract

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.