Entanglement assisted training algorithm for supervised quantum classifiers

TL;DR

This paper introduces an entanglement-based training algorithm for supervised quantum classifiers that leverages quantum entanglement and Bell-inequality-based cost functions to improve classification performance.

Contribution

It presents a novel quantum training method utilizing entanglement and Bell inequalities, enabling simultaneous error encoding for multiple samples, which is a new approach in quantum classification.

Findings

Achieved successful binary classification on benchmark datasets.

Demonstrated the feasibility of entanglement-assisted training in quantum classifiers.

Potential extension to multi-class classification discussed.

Abstract

We propose a new training algorithm for supervised quantum classifiers. Here, we have harnessed the property of quantum entanglement to build a model that can simultaneously manipulate multiple training samples along with their labels. Subsequently a Bell-inequality based cost function is constructed, that can encode errors from multiple samples, simultaneously, in a way that is not possible by any classical means. We show that upon minimizing this cost function one can achieve successful classification in benchmark datasets. The results presented in this paper are for binary classification problems. Nevertheless, the analysis can be extended to multi-class classification problems as well.