QMViT: A Mushroom is worth 16x16 Words

TL;DR

This paper introduces QMViT, a quantum-enhanced vision transformer that significantly improves the accuracy of edible versus toxic mushroom classification, reducing false negatives and enhancing food safety.

Contribution

The paper presents a novel quantum vision transformer architecture utilizing Variational Quantum Circuits for improved mushroom classification accuracy.

Findings

Achieved 92.33% accuracy in mushroom category classification.

Achieved 99.24% accuracy in edibility classification.

Demonstrated reduced false negatives for toxic mushrooms.

Abstract

Consuming poisonous mushrooms can have severe health consequences, even resulting in fatality and accurately distinguishing edible from toxic mushroom varieties remains a significant challenge in ensuring food safety. So, it's crucial to distinguish between edible and poisonous mushrooms within the existing species. This is essential due to the significant demand for mushrooms in people's daily meals and their potential contributions to medical science. This work presents a novel Quantum Vision Transformer architecture that leverages quantum computing to enhance mushroom classification performance. By implementing specialized quantum self-attention mechanisms using Variational Quantum Circuits, the proposed architecture achieved 92.33% and 99.24% accuracy based on their category and their edibility respectively. This demonstrates the success of the proposed architecture in reducing false negatives for toxic mushrooms, thus ensuring food safety. Our research highlights the potential of QMViT for improving mushroom classification as a whole.