  We propose and analyse simple deterministic algorithms that can be used to
construct machines that have primitive learning capabilities. We demonstrate
that locally connected networks of these machines can be used to perform blind
classification on an event-by-event basis, without storing the information of
the individual events. We also demonstrate that properly designed networks of
these machines exhibit behavior that is usually only attributed to quantum
systems. We present networks that simulate quantum interference on an
event-by-event basis. In particular we show that by using simple geometry and
the learning capabilities of the machines it becomes possible to simulate
single-photon interference in a Mach-Zehnder interferometer. The interference
pattern generated by the network of deterministic learning machines is in
perfect agreement with the quantum theoretical result for the single-photon
Mach-Zehnder interferometer. To illustrate that networks of these machines are
indeed capable of simulating quantum interference we simulate, event-by-event,
a setup involving two chained Mach-Zehnder interferometers. We show that also
in this case the simulation results agree with quantum theory.
