OptiQKD: A Machine Learning-Optimized Framework for Real-Time Parameter Tuning in Quantum Key Distribution

TL;DR

OptiQKD is a machine learning framework that dynamically optimizes quantum key distribution parameters in real-time, significantly improving key rates and reducing errors while maintaining security under various environmental conditions.

Contribution

The paper introduces OptiQKD, a novel, protocol-agnostic machine learning approach combining TCNs and reinforcement learning for real-time quantum parameter optimization.

Findings

Median SKR increases by 20-30%

QBER reduces from 3.0% to 1.5%

Effective under simulated environmental stressors

Abstract

Despite the robust security guarantees of Quantum Key Distribution (QKD), its practical deployment is significantly challenged by the dynamic nature of quantum channels and the complexity of real-time parameter optimization. In this paper, we propose OptiQKD, a protocol-agnostic machine learning framework specifically engineered to maximize the Secure Key Rate (SKR) and minimize the Quantum Bit Error Rate (QBER) for the BB84, E91, and COW protocols. OptiQKD integrates Temporal Convolutional Networks (TCNs) for high-accuracy and short-horizon forecasting of channel-state fluctuations with a Reinforcement Learning (RL) controller for autonomous and real-time parameter selection. This optimization stack is strictly constrained by standard composable-security assumptions to ensure that performance gains do not compromise the underlying quantum security. We evaluate the framework by simulating critical environmental stressors, including depolarizing and amplitude-damping noise, under realistic device constraints, including channel loss, detector efficiency, and dark counts. Our results demonstrate substantial protocol-agnostic improvements: the median SKR increases by 20--30%, while the median QBER is reduced from 3.0% to 1.5% through predictive state optimization. These findings establish that OptiQKD provides an efficient, security-preserving mechanism for dynamic parameter tuning, paving the way for more resilient and high-throughput practical QKD deployments.