Information Reconciliation is a mechanism that allows to weed out the discrepancies between two correlated variables. It is an essential component in every key agreement protocol where the key has to be transmitted through a noisy channel. The typical case is in the satellite scenario described by Maurer in the early 90's. Recently the need has arisen in relation with Quantum Key Distribution (QKD) protocols, where it is very important not to reveal unnecessary information in order to maximize the shared key length. In this paper we present an information reconciliation protocol based on a rate compatible construction of Low Density Parity Check codes. Our protocol improves the efficiency of the reconciliation for the whole range of error rates in the discrete variable QKD context. Its adaptability together with its low interactivity makes it specially well suited for QKD reconciliation.
To perform Quantum Key Distribution, the mastering of the extremely weak signals carried by the quantum channel is required. Transporting these signals without disturbance is customarily done by isolating the quantum channel from any noise sources using a dedicated physical channel. However, to really profit from this technology, a full integration with conventional network technologies would be highly desirable. Trying to use single photon signals with others that carry an average power many orders of magnitude bigger while sharing as much infrastructure with a conventional network as possible brings obvious problems. The purpose of the present paper is to report our efforts in researching the limits of the integration of QKD in modern optical networks scenarios. We have built a full metropolitan area network testbed comprising a backbone and an access network. The emphasis is put in using as much as possible the same industrial grade technology that is actually used in already installed networks, in order to understand the throughput, limits and cost of deploying QKD in a real network.
In activation calculations, there are several approaches to quantify uncertainties: deterministic by means of sensitivity analysis, and stochastic by means of Monte Carlo. Here, two different Monte Carlo approaches for nuclear data uncertainty are presented: the first one is the Total Monte Carlo (TMC). The second one is by means of a Monte Carlo sampling of the covariance information included in the nuclear data libraries to propagate these uncertainties throughout the activation calculations. This last approach is what we named Covariance Uncertainty Propagation, CUP.This work presents both approaches and their differences. Also, they are compared by means of an activation calculation, where the cross-section uncertainties of 239 Pu and 241 Pu are propagated in an ADS activation calculation.
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