  We consider adiabatic superconducting cells operating as an artificial neuron
and synapse of a multilayer perceptron (MLP). Their compact circuits contain
just one and two Josephson junctions, respectively. While the signal is
represented as magnetic flux, the proposed cells are inherently nonlinear and
close-to-linear magnetic flux transformers. The neuron is capable of providing
a one-shot calculation of sigmoid and hyperbolic tangent activation functions
most commonly used in MLP. The synapse features by both positive and negative
signal transfer coefficients in the range ~ (-0.5,0.5). We briefly discuss
implementation issues and further steps toward multilayer adiabatic
superconducting artificial neural network which promises to be a compact and
the most energy-efficient implementation of MLP.
