Optimizing Continuous-Wave Pumped Entanglement-based QKD in Noisy Environment

• URL: http://arxiv.org/abs/2502.15059v3
• Date: Mon, 10 Mar 2025 19:40:12 GMT
• Title: Optimizing Continuous-Wave Pumped Entanglement-based QKD in Noisy Environment
• Authors: Hashir Kuniyil, Saif Al-Kuwari, Asad Ali, Artur Czerwinski, Syed M. Arslan,
• Abstract summary: Quantum key distribution (QKD) has emerged as a promising solution to protect cryptographic systems against the threat of quantum computers.<n>In this paper, we investigate the impact of extreme noise on QKD system parameters, including detector timing uncertainty (jitter)<n>We show that changes in these parameters play a key role in determining system performance in noisy environments.
• Score: 0.6291443816903801
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Quantum key distribution (QKD) has emerged as a promising solution to protect current cryptographic systems against the threat of quantum computers. As QKD transitions from laboratories to real-world applications, its implementation under various environmental conditions has become a pressing challenge. Major obstacles to practical QKD implementation are the loss of photons in the transmission media and the presence of extreme noise, which can severely limit long-range transmission. In this paper, we investigate the impact of extreme noise on QKD system parameters, including detector timing uncertainty (jitter), measurement timing shifts, variations in detector deadtime, and rate-dependent detector efficiency. Contrary to manufacturers' specifications, which assume these parameters to be constant, we demonstrate that these parameters exhibit significant variations in extreme noise conditions. We show that changes in these parameters play a key role in determining system performance in noisy environments. To address these non-idealities, we develop a robust model that can address and adapt to various detector characteristics. In particular, our model is independent of source parameters and can be implemented using data from the detection unit. Our results show that our model is well suited for characterizing and optimizing the performance of the QKD system under noisy conditions.

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