  Perceptrons are the basic computational unit of artificial neural networks,
as they model the activation mechanism of an output neuron due to incoming
signals from its neighbours. As linear classifiers, they play an important role
in the foundations of machine learning. In the context of the emerging field of
quantum machine learning, several attempts have been made to develop a
corresponding unit using quantum information theory. Based on the quantum phase
estimation algorithm, this paper introduces a quantum perceptron model
imitating the step-activation function of a classical perceptron. This scheme
requires resources in $\mathcal{O}(n)$ (where $n$ is the size of the input) and
promises efficient applications for more complex structures such as trainable
quantum neural networks.
