Pattern-Recognition Training of a Quantum Neuron on a Quantum Computer

The use of advanced quantum neuron models for pattern recognition applications require fault tolerant quantum coomputing. Such models are challenging to implement on currently available quantum processors due to noise introduced by non-local operations. We propose an alternative quantum perceptron (QP) model that uses a reduced number of multi-qubit gates and is less susceptible to quantum errors than other existing models. We demonstrate the performance of the proposed model through an implemention of the QP on a few qubits using an actual quantum computer. The proposed QP uses an N-ary encoding of the binary input data characterizing the patterns. We develop various hybrid (quantum-classical) training procedures for simulating the learning process of the QP and test their efficiency. We also provide a comparative analysis of the required quantum error corrections for scalability.