Development of a Novel Quantum Pre-processing Filter to Improve Image Classification Accuracy of Neural Network Models

TL;DR

This paper introduces a quantum pre-processing filter that enhances neural network image classification accuracy on simple datasets like MNIST and EMNIST, but faces challenges with complex real-world data, suggesting avenues for future research.

Contribution

The paper presents a novel quantum feature extraction filter that improves neural network accuracy without adding model complexity.

Findings

Improved accuracy on MNIST from 92.5% to 95.4%.

Enhanced EMNIST accuracy from 68.9% to 75.9%.

Degradation observed on complex GTSRB dataset.

Abstract

This paper proposes a novel quantum pre-processing filter (QPF) to improve the image classification accuracy of neural network (NN) models. A simple four qubit quantum circuit that uses Y rotation gates for encoding and two controlled NOT gates for creating correlation among the qubits is applied as a feature extraction filter prior to passing data into the fully connected NN architecture. By applying the QPF approach, the results show that the image classification accuracy based on the MNIST (handwritten 10 digits) and the EMNIST (handwritten 47 class digits and letters) datasets can be improved, from 92.5% to 95.4% and from 68.9% to 75.9%, respectively. These improvements were obtained without introducing extra model parameters or optimizations in the machine learning process. However, tests performed on the developed QPF approach against a relatively complex GTSRB dataset with 43 distinct class real-life traffic sign images showed a degradation in the classification accuracy. Considering this result, further research into the understanding and the design of a more suitable quantum circuit approach for image classification neural networks could be explored utilizing the baseline method proposed in this paper.