Non-binary artificial neuron with phase variation implemented on a quantum computer

Abstract

The first artificial quantum neuron models followed a similar path to classic models and they work only with discrete values. Here we introduce an algorithm that generalizes the binary model manipulating the phase of complex numbers. We propose, test, and implement a neuron model that works with continuous values in a quantum computer. Through simulations, we also demonstrated that our model may work in a hybrid training scheme utilizing gradient descent as a learning algorithm. This work represents another step in the direction of the practical use of artificial neural networks efficiently implemented in near-term quantum devices.