  Artificial neural networks have been proposed as potential algorithms that
could benefit from being implemented and run on quantum computers. In
particular, they hold promise to greatly enhance Artificial Intelligence tasks,
such as image elaboration or pattern recognition. The elementary building block
of a neural network is an artificial neuron, i.e. a computational unit
performing simple mathematical operations on a set of data in the form of an
input vector. Here we show how the design for the implementation of a
previously introduced quantum artificial neuron [npj Quant. Inf. $\textbf{5}$,
26], which fully exploits the use of superposition states to encode binary
valued input data, can be further generalized to accept continuous -- instead
of discrete-valued input vectors, without increasing the number of qubits. This
further step is crucial to allow for a direct application of an automatic
differentiation learning procedure, which would not be compatible with
binary-valued data encoding.
