# Binary Classifier Inspired by Quantum Theory

## TL;DR

This paper introduces a novel binary classifier inspired by quantum theory that enhances classification recall across categories, suggesting potential improvements over traditional ML models based on classical probability.

## Contribution

The paper proposes the BCIQT model, integrating quantum theory principles into binary classification to improve recall performance over existing classical ML classifiers.

## Key findings

- Outperforms state-of-the-art classifiers in recall
- Demonstrates the effectiveness of quantum-inspired approaches in ML
- Shows potential for broader application in pattern recognition

## Abstract

Machine Learning (ML) helps us to recognize patterns from raw data. ML is used in numerous domains i.e. biomedical, agricultural, food technology, etc. Despite recent technological advancements, there is still room for substantial improvement in prediction. Current ML models are based on classical theories of probability and statistics, which can now be replaced by Quantum Theory (QT) with the aim of improving the effectiveness of ML. In this paper, we propose the Binary Classifier Inspired by Quantum Theory (BCIQT) model, which outperforms the state of the art classification in terms of recall for every category.