Development of a Novel Quantum Pre-processing Filter to Improve Image
  Classification Accuracy of Neural Network Models
Farina Riaz Shahab Abdulla Hajime Suzuki Srinjoy Ganguly Ravinesh C. Deo Susan Hopkins
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.