  The Kak family of neural networks is able to learn patterns quickly, and this
speed of learning can be a decisive advantage over other competing models in
many applications. Amongst the implementations of these networks are those
using reconfigurable networks, FPGAs and optical networks. In some
applications, it is useful to use complex data, and it is with that in mind
that this introduction to the basic Kak network with complex inputs is being
presented. The training algorithm is prescriptive and the network weights are
assigned simply upon examining the inputs. The input is mapped using quaternary
encoding for purpose of efficienty. This network family is part of a larger
hierarchy of learning schemes that include quantum models.
