  We present a model of Continuous Variable Quantum Perceptron (CVQP) whose
architecture implements a classical perceptron. The necessary non-linearity is
obtained via measuring the output qubit and using the measurement outcome as
input to an activation function. The latter is chosen to be the so-called ReLu
activation function by virtue of its practical feasibility and the advantages
it provides in learning tasks. The encoding of classical data into realistic
finitely squeezed states and the use of superposed (entangled) input states for
specific binary problems are discussed.
