This paper proposes a brain-inspired approach to quantum machine learning
with the goal of circumventing many of the complications of other approaches.
The fact that quantum processes are unitary presents both opportunities and
challenges. A principal opportunity is that a large number of computations can
be carried out in parallel in linear superposition, that is, quantum
parallelism. The challenge is that the process is linear, and most approaches
to machine learning depend significantly on nonlinear processes. Fortunately,