This thesis deals with soft-information based information reconciliation and data sifting for
Quantum Key Distribution (QKD). A novel composite channel model for QKD is identified, which
includes both a hard output quantum channel and a soft output classic channel. The Log-Likelihood
Ratios, - also called soft-metrics - derived from the two channels are jointly processed at the receiver,
exploiting capacity achieving soft-metric based iteratively decoded block codes. The performance
of the proposed mixed-soft-metric algorithms are studied via simulations as a function of the system
parameters.
The core ideas of the thesis are employing Forward Error Correction (FEC) coding as opposed to
two-way communication for information reconciliation in QKD schemes, exploiting all the available
information for data processing at the receiver including information available from the quantum
channel, since optimized use of this information can lead to significant performance improvement,
and providing a security versus secret-key rate trade-off to the end-user within the context of QKD
systems
