  With the onset of gated quantum machine learning, the architecture for such a
system is an open question. Many architectures are created either ad hoc or are
directly analogous from known classical architectures. Presented here is a
novel algorithm which learns a gated quantum machine learning architecture
while simultaneously learning its parameters. This proof of concept and some of
its variations are explored and discussed.
