  The utility of classical neural networks as universal approximators suggests
that their quantum analogues could play an important role in quantum
generalizations of machine-learning methods. Inspired by the proposal in
[Torrontegui and Garc\'ia-Ripoll 2019 EPL 125 30004], we demonstrate a
superconducting qubit implementation of an adiabatic controlled gate, which
generalizes the action of a classical perceptron as the basic building block of
a quantum neural network. We show full control over the steepness of the
perceptron activation function, the input weight and the bias by tuning the
adiabatic gate length, the coupling between the qubits and the frequency of the
applied drive, respectively. In its general form, the gate realizes a
multi-qubit entangling operation in a single step, whose decomposition into
single- and two-qubit gates would require a number of gates that is exponential
in the number of qubits. Its demonstrated direct implementation as perceptron
in quantum hardware may therefore lead to more powerful quantum neural networks
when combined with suitable additional standard gates.
