Characterization of QBER contributions of phase-encoding QKD transmitters with an optical coherent receiver
Year: 2026
Type: conference-paper
Abstract: The technological development of QKD (Quantum Key Distribution) is accelerating from a research topic towards commercial deployment. QKD initiatives are increasingly addressing real-world use cases. Besides lots of literature on theory and lab-demos, first high Technology-Readiness Level systems are available. Due to the security guarantees it has to provide, the performance of any QKD implementation is particularly sensitive to any imperfections that may lead to an increase of the QBER (Quantum Bit Error Rate). Achieving high performance, or a low QBER value, constitutes a challenging engineering task, as the operating systems need to be highly optimized in many aspects. In particular, accurate phase modulation is the foundation of high system performance in case of phase-encoding protocols. In most cases, the overall QBER is measured with a QKD receiver, which accumulates errors from transmitter, quantum channel and receiver. This requires an operational transmitter system and additionally prevents the investigation of the pure transmitter output signal’s characteristics. However, for support of transmitter integration and test activities as well as for validation tasks, a more suitable method remains to be developed. In this paper, we propose such a method by making use of a coherent receiver. This device provides extended signal information including the signal amplitude and its current relative phase. Therefore, it is possible to monitor phase-encoding correctness and precision, among other relevant signal properties. We analyze the reasonability of using a coherent receiver, by outlining its capabilites to measure QBER contributors occurring in a given basic reference QKD transmitter system. Our findings indicate that employing a coherent receiver is a simple method to accelerate development and validation of phase-encoding QKD transmitter systems.
DOI: 10.1117/12.3114584
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7221068719
