  Computing key rates in quantum key distribution (QKD) numerically is
essential to unlock more powerful protocols, that use more sophisticated
measurement bases or quantum systems of higher dimension. It is a difficult
optimization problem, that depends on minimizing a convex non-linear function:
the (quantum) relative entropy. Standard conic optimization techniques have for
a long time been unable to handle the relative entropy cone, as it is a
non-symmetric cone, and the standard algorithms can only handle symmetric ones.
Recently, however, a practical algorithm has been discovered for optimizing
over non-symmetric cones, including the relative entropy. Here we adapt this
algorithm to the problem of computation of key rates, obtaining an efficient
technique for lower bounding them. In comparison to previous techniques it has
the advantages of flexibility, ease of use, and above all performance.
