Supervised learning with a quantum classifier using a multi-level system

TL;DR

This paper introduces a quantum classifier that encodes data in a multi-level quantum system, uses a hybrid variational training method, and demonstrates faster training and fewer parameters compared to classical classifiers.

Contribution

It presents a novel quantum classifier using a single qu$N$it system with single shot training, improving speed and parameter efficiency over classical methods.

Findings

Successfully classified benchmark datasets

Faster training due to single shot approach

Fewer training parameters than classical classifiers

Abstract

We propose a quantum classifier, which can classify data under the supervised learning scheme using a quantum feature space. The input feature vectors are encoded in a single qu$N$it (a $N$ level quantum system), as opposed to more commonly used entangled multi-qubit systems. For training we use the much used quantum variational algorithm -- a hybrid quantum-classical algorithm -- in which the forward part of the computation is performed on a quantum hardware whereas the feedback part is carried out on a classical computer. We introduce "single shot training" in our scheme, with all input samples belonging to the same class being used to train the classifier simultaneously. This significantly speeds up the training procedure and provides an advantage over classical machine learning classifiers. We demonstrate successful classification of popular benchmark datasets with our quantum classifier and compare its performance with respect to some classical machine learning classifiers. We also show that the number of training parameters in our classifier is significantly less than the classical classifiers.