5.4QUANT-PHMar 30
Compact Continuous-Variable Quantum Key Distribution System Employing Monolithically Integrated Silicon Photonic TransceiverDenis Fatkhiev, João dos Reis Frazão, Alireza H. Derkani et al.
We demonstrate the first CV-QKD system featuring a custom-designed monolithic silicon photonic dual-polarisation transceiver. Leveraging PS-64-QAM, we achieved 1.9 Mbit/s secret key rate across 25 km of standard single-mode fibre, highlighting the potential of electronic-photonic integration for practical QKD.
6.0OPTICSJun 15
Reducing Turbulence-Induced Outages in a Deployed Terrestrial Free-Space Optical Communication Link via InterleavingKadir Gümüş, Vincent van Vliet, Menno van den Hout et al.
We present an experimental study of data interleaving for terrestrial free-space optical communication over a 4.6~km urban testbed. Results demonstrate a two-order-of-magnitude reduction in outage probability. A dependency between measured turbulence strength, interleaver length, and achievable data rate is revealed, enabling robust system design.
Low Rate Protograph-Based LDPC Codes for Continuous Variable Quantum Key DistributionKadir Gümüs, Laurent Schmalen
Error correction plays a major role in the reconciliation of continuous variable quantum key distribution (CV-QKD) and greatly affects the performance of the system. CV-QKD requires error correction codes of extremely low rates and high reconciliation efficiencies. There are only very few code designs available in this ultra low rate regime. In this paper, we introduce a method for designing protograph-based ultra low rate LDPC codes using differential evolution. By proposing type-based protographs, a new way of representing low rate protograph-based LDPC codes, we drastically reduce the complexity of the protograph optimization, which enables us to quickly design codes over a wide range of rates. We show that the codes resulting from our optimization outperform the codes from the literature both in regards to the threshold and in finite-length performance, validated by Monte-Carlo simulations, showing gains in the regime relevant for CV-QKD.
3.3SPDec 11, 2019
End-to-End Learning of Geometrical Shaping Maximizing Generalized Mutual InformationKadir Gümüs, Alex Alvarado, Bin Chen et al.
GMI-based end-to-end learning is shown to be highly nonconvex. We apply gradient descent initialized with Gray-labeled APSK constellations directly to the constellation coordinates. State-of-the-art constellations in 2D and 4D are found providing reach increases up to 26\% w.r.t. to QAM.