  The use of advanced quantum neuron models for pattern recognition
applications requires fault tolerance. Therefore, it is not yet possible to
test such models on a large scale in currently available quantum processors. As
an alternative, we propose a quantum perceptron (QP) model that uses a reduced
number of multi-qubit gates and is therefore less susceptible to quantum errors
in current actual quantum computers with limited tolerance. The proposed
quantum algorithm is superior to its classical counterpart, although since it
does not take full advantage of quantum entanglement, it provides a lower
encoding power than other quantum algorithms using multiple qubit entanglement.
However, the use of separable-sate encoding allows for testing the algorithm
and different training schemes at a large scale in currently available
non-fault tolerant quantum computers. We demonstrate the performance of the
proposed model by implementing a few qubits version of the QP in a simulated
quantum computer. The proposed QP uses an N-ary encoding of the binary input
data characterizing the patterns. We develop a hybrid (quantum-classical)
training procedure for simulating the learning process of the QP and test their
efficiency.
