Variational Quantum Classifier

Abstract

In this work we propose a quantum alternative to Artificial Neural Networks in classification tasks. We design a set of different neural networks and quantum circuits and test their performances. We found that a Variational Quantum Classifier can outperform a classical model using far less free parameters and, thus, being more eficient. Further, a complex classification task requires deeper quantum circuits, which nevertheless grow at a slower pace than the number of neurons needed in a Neural Network for the same task.