Application of Quantum Pre-Processing Filter for Binary Image
  Classification with Small Samples
Farina Riaz Shahab Abdulla Hajime Suzuki Srinjoy Ganguly Ravinesh C. Deo Susan Hopkins
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.