A New Quantum Approach to Binary Classification

TL;DR

This paper introduces a novel quantum-inspired binary classifier that leverages principles of quantum mechanics to potentially enhance classical machine learning classification performance.

Contribution

It presents a new quantum-inspired approach to binary classification, bridging quantum theory and machine learning for improved algorithms.

Findings

Proposes a quantum-inspired binary classifier.

Demonstrates potential improvements over classical methods.

Lays groundwork for future quantum machine learning research.

Abstract

Machine Learning classification models learn the relation between input as features and output as a class in order to predict the class for the new given input. Quantum Mechanics (QM) has already shown its effectiveness in many fields and researchers have proposed several interesting results which cannot be obtained through classical theory. In recent years, researchers have been trying to investigate whether the QM can help to improve the classical machine learning algorithms. It is believed that the theory of QM may also inspire an effective algorithm if it is implemented properly. From this inspiration, we propose the quantum-inspired binary classifier.