Error estimation at the information reconciliation stage of quantum key distribution
October 13, 2018 Β· Declared Dead Β· π Journal of Russian Laser Research
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
E. O. Kiktenko, A. O. Malyshev, A. A. Bozhedarov, N. O. Pozhar, M. N. Anufriev, A. K. Fedorov
arXiv ID
1810.05841
Category
quant-ph: Quantum Computing
Cross-listed
cs.IT
Citations
19
Venue
Journal of Russian Laser Research
Last Checked
4 months ago
Abstract
Quantum key distribution (QKD) offers a practical solution for secure communication between two distinct parties via a quantum channel and an authentic public channel. In this work, we consider different approaches to the quantum bit error rate (QBER) estimation at the information reconciliation stage of the post-processing procedure. For reconciliation schemes employing low-density parity-check (LDPC) codes, we develop a novel syndrome-based QBER estimation algorithm. The algorithm suggested is suitable for irregular LDPC codes and takes into account punctured and shortened bits. Testing our approach in a real QKD setup, we show that an approach combining the proposed algorithm with conventional QBER estimation techniques allows one to improve the accuracy of the QBER estimation.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Quantum Computing
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Quantum machine learning: a classical perspective
R.I.P.
π»
Ghosted
Noise-Adaptive Compiler Mappings for Noisy Intermediate-Scale Quantum Computers
R.I.P.
π»
Ghosted
ProjectQ: An Open Source Software Framework for Quantum Computing
R.I.P.
π»
Ghosted
Quantum Recommendation Systems
R.I.P.
π»
Ghosted
Traffic flow optimization using a quantum annealer
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted