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

2024-12-18

2024 8th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI)

AI Summary

This work addresses the significant performance limitations of conventional convolutional neural networks (CNNs) in few-shot image classification due to extreme data scarcity. To overcome this challenge, the authors propose a novel architecture that integrates a projected quantum kernel (PQK) into the CNN’s feature extraction module for the first time, leveraging the quantum kernel’s expressive power in capturing high-dimensional and complex data structures to enhance representational capacity under minimal training data. Experimental results demonstrate that with only 1,000 training samples, the proposed method achieves 95% accuracy on MNIST and 90% on CIFAR-10, substantially outperforming classical CNNs, which attain merely 60% and 12% accuracy, respectively. This approach effectively breaks through the performance bottleneck typically encountered in data-scarce scenarios.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Non-convex Optimization

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

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.

Problem

• small dataset
• image classification
• data scarcity
• convolutional neural networks
• limited data

Innovation

• Projected Quantum Kernels
• Convolutional Neural Networks
• Small Dataset Classification
• Quantum Machine Learning
• Feature Extraction