Application of Quantum Pre-Processing Filter for Binary Image Classification with Small Samples

TL;DR

This paper explores the use of a quantum pre-processing filter (QPF) to enhance binary image classification accuracy across various datasets, especially with small sample sizes, demonstrating mixed results depending on dataset complexity.

Contribution

The study applies a quantum pre-processing filter to binary image classification, showing its potential to improve accuracy on certain datasets with limited samples, extending previous multi-class results.

Findings

QPF improved accuracy on MNIST, EMNIST, and CIFAR-10 with full samples.

QPF enhanced performance on CIFAR-10 and GTSRB with small samples.

QPF did not improve accuracy on MNIST and EMNIST with small samples.

Abstract

Over the past few years, there has been significant interest in Quantum Machine Learning (QML) among researchers, as it has the potential to transform the field of machine learning. Several models that exploit the properties of quantum mechanics have been developed for practical applications. In this study, we investigated the application of our previously proposed quantum pre-processing filter (QPF) to binary image classification. We evaluated the QPF on four datasets: MNIST (handwritten digits), EMNIST (handwritten digits and alphabets), CIFAR-10 (photographic images) and GTSRB (real-life traffic sign images). Similar to our previous multi-class classification results, the application of QPF improved the binary image classification accuracy using neural network against MNIST, EMNIST, and CIFAR-10 from 98.9% to 99.2%, 97.8% to 98.3%, and 71.2% to 76.1%, respectively, but degraded it against GTSRB from 93.5% to 92.0%. We then applied QPF in cases using a smaller number of training and testing samples, i.e. 80 and 20 samples per class, respectively. In order to derive statistically stable results, we conducted the experiment with 100 trials choosing randomly different training and testing samples and averaging the results. The result showed that the application of QPF did not improve the image classification accuracy against MNIST and EMNIST but improved it against CIFAR-10 and GTSRB from 65.8% to 67.2% and 90.5% to 91.8%, respectively. Further research will be conducted as part of future work to investigate the potential of QPF to assess the scalability of the proposed approach to larger and complex datasets.