  This study concerns with the dynamics of a quantum neural network unit in
order to examine the suitability of simple neural computing tasks. More
specifically, we examine the dynamics of an interacting spin model chosen as a
candidate of a quantum perceptron for closed and open quantum systems. We adopt
a collisional model enables examining both Markovian and non-Markovian dynamics
of the proposed quantum system. We show that our quantum neural network (QNN)
unit has a stable output quantum state in contact with an environment carrying
information content. By the performed numerical simulations one can compare the
dynamics in the presence and absence of quantum memory effects. We find that
our QNN unit is suitable for implementing general neural computing tasks in
contact with a Markovian information environment and quantum memory effects
cause complications on the stability of the output state.
