Information reconciliation for continuous variable QKD systems based on permutation modulation

Abstract

This paper is focused on the problem of Information Reconciliation (IR) for continuous variable Quantum Key Distribution (QKD). The main problem is quantization and assignment of labels to the samples of the Gaussian variables observed at Alice and Bob. Trouble is that most of the samples, assuming that the Gaussian variable is zero mean which is de-facto the case, tend to have small magnitudes and are easily disturbed by noise. Transmission over longer and longer distances increase the losses corresponding to a lower effective Signal to Noise Ratio (SNR) exasperating the problem. In previous work [1], [2] we approach the IR problem in a fundamentally different way than how it is addressed in published literature. At very low SNR, one can focus exclusively on the sign of the Gaussian samples at Alice and Bob and perform Reverse Reconciliation (RR) on sign of the samples given their quantized magnitudes as side information Alice and Bob share in the open. In this paper we demonstrate that the weighted sum capacity of the Binary Symmetric Channels (BSCs) derived from magnitude dependent sign error probabilities of the Gaussian samples shared between Alice and Bob approaches the mutual information between Alice and Bob at very low SNR regimes. Thus, when SNR is very low, one could just as well ignore any common randomness that may be obtained from magnitude of the Gaussian samples and focus exclusively on their sign. The caveat is, to get close to this mutual information, Unequal Error Protection (UEP) must be employed based on magnitude of the observed samples at Bob in RR.