Enhancing Small Dataset Classification Using Projected Quantum Kernels with Convolutional Neural Networks

TL;DR

This paper introduces a novel method combining projected quantum kernels with CNNs to significantly improve image classification accuracy on small datasets, demonstrating the potential of quantum computing in machine learning.

Contribution

It presents an innovative integration of projected quantum kernels into CNNs, enhancing feature extraction for small dataset classification, which is a novel approach in quantum-assisted neural networks.

Findings

Achieved 95% accuracy on MNIST with 1000 samples

Outperformed classical CNNs significantly on small datasets

Demonstrated potential of quantum kernels in data-scarce environments

Abstract

Convolutional Neural Networks (CNNs) have shown promising results in efficiency and accuracy in image classification. However, their efficacy often relies on large, labeled datasets, posing challenges for applications with limited data availability. Our research addresses these challenges by introducing an innovative approach that leverages projected quantum kernels (PQK) to enhance feature extraction for CNNs, specifically tailored for small datasets. Projected quantum kernels, derived from quantum computing principles, offer a promising avenue for capturing complex patterns and intricate data structures that traditional CNNs might miss. By incorporating these kernels into the feature extraction process, we improved the representational ability of CNNs. Our experiments demonstrated that, with 1000 training samples, the PQK-enhanced CNN achieved 95% accuracy on the MNIST dataset and 90% on the CIFAR-10 dataset, significantly outperforming the classical CNN, which achieved only 60% and 12% accuracy on the respective datasets. This research reveals the potential of quantum computing in overcoming data scarcity issues in machine learning and paves the way for future exploration of quantum-assisted neural networks, suggesting that projected quantum kernels can serve as a powerful approach for enhancing CNN-based classification in data-constrained environments.